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920-324 - Communication Server (CS) Rls. 4.0 Database Administrator - Dump Information

Vendor : Nortel
Exam Code : 920-324
Exam Name : Communication Server (CS) Rls. 4.0 Database Administrator
Questions and Answers : 58 Q & A
Updated On : April 17, 2019
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920-324 Communication Server (CS) Rls. 4.0 Database Administrator

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920-324 exam Dumps Source : Communication Server (CS) Rls. 4.0 Database Administrator

Test Code : 920-324
Test Name : Communication Server (CS) Rls. 4.0 Database Administrator
Vendor Name : Nortel
Q&A : 58 Real Questions

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Nortel Communication Server (CS) Rls.

Microsoft and Nortel Make Unified Communications available to agencies far and wide | killexams.com Real Questions and Pass4sure dumps

LAS VEGAS — June sixteen, 2008 — these days at NXTcomm08, Microsoft Corp. and Nortel introduced a totally hosted service solution that delivers a unified communications event to small and midsize agencies (SMBs), extending benefits prior to now available best to gigantic businesses. The hosted answer is based on the Nortel Communications Server 2000 (CS 2000), an internet Protocol multimedia softswitch, and the Microsoft answer for Hosted Messaging and Collaboration edition 4.5 (HMC 4.5), which comprises hosted models of Microsoft workplace Communications Server 2007 and Microsoft alternate Server 2007. Nortel is delivering the first integration with HMC 4.5 to assist manage real-time communications and convey a unified messaging adventure. furthermore, to aid carriers simplify and speed up deployment, Nortel’s international features portfolio offers a finished suite of integration and conclusion-user services to complete the conclusion-to-end solution.

via combining the carrier-grade CS 2000 with HMC four.5, service providers can offer SMBs new, effective unified communications that mix real-time communications, equivalent to voice, with Microsoft’s collaboration capabilities, corresponding to click-to-call, click-to-conference, overlooked name electronic mail messages, mobility and telephony presence. These facets all work seamlessly with the Microsoft office portfolio and the Microsoft workplace Communicator customer to carry a unique journey to the end person. This new answer also enables SMBs to advantage from unified communications while not having to purchase, set up, manage and maintain additional network infrastructure and complex in-residence methods, enabling them to shop operational prices and boost productivity.

“Unified communications is a strategic buy that, up except nowadays, has been leveraged essentially by using the huge business,” talked about Ruchi Prasad, vp and regularly occurring manager of the imaginitive Communications Alliance at Nortel. “This improved joint Microsoft and Nortel solution makes unified communications attainable to agencies of all sizes. For carrier providers, this answer presents new salary opportunities and the means to expand their addressable market and bring higher-cost utility and repair bundles to their customers.”

“we're currently seeing mighty hobby within the hosted unified communications solution amongst our provider company purchasers,” referred to Steve Zimba, managing director, global Telecom company, Microsoft. “The totally hosted unified communications answer gives opportunities for extended revenue and helps carrier providers deliver the related work style to SMBs, offering the ability to talk virtually every time, anyplace and on any equipment.”

At NXTcomm08, Microsoft and Nortel will exhibit the hosted unified communications answer and show its value in bettering business agility. NXTcomm08 attendees can reap additional information with the aid of touring the Microsoft booths SL5916 and SL5923 and the Nortel sales space SL3516. more information can also be discovered at http://www.microsoft.com/serviceproviders/nxtcomm08.

About Nortel

Nortel is a identified leader in delivering communications capabilities that make the promise of business Made simple a fact for our shoppers. Our next-generation technologies, for each service provider and commercial enterprise networks, support multimedia and company-essential applications. Nortel’s applied sciences are designed to support eliminate these days’s obstacles to efficiency, velocity and efficiency with the aid of simplifying networks and connecting americans to the information they want, once they want it. Nortel does company in additional than one hundred fifty nations around the world. For greater suggestions, consult with Nortel on the net at www.nortel.com. For the newest Nortel information, discuss with www.nortel.com/information.

About Microsoft

headquartered in 1975, Microsoft (Nasdaq “MSFT”) is the worldwide chief in utility, features and options that assist individuals and organizations recognize their full capabilities.

word to editors: in case you are interested in viewing more information on Microsoft, please discuss with the Microsoft internet web page at http://www.microsoft.com/presspass on Microsoft’s company tips pages. web links, mobile numbers and titles were correct at time of booklet, however can also since have modified. For further advice, journalists and analysts can also contact Microsoft’s fast Response team or other appropriate contacts listed at http://www.microsoft.com/presspass/contactpr.mspx.

LG-Nortel Unveils Line of desktop IP telephones | killexams.com Real Questions and Pass4sure dumps

the brand new standards-compliant IP telephone 8800 collection with the aid of LG-Nortel, a three way partnership of LG Electronics (LGERF.PK) and Nortel (NT), grants IP telephony performance for organizations, SMBs and carrier-hosted VoIP features.

Incorporation of a wide-band codec allows for the LG-Nortel IP mobile 8800 collection to convey the most advantageous voice best continually, the company pointed out.

The LG-Nortel IP mobilephone 8800 sequence supports commonplace protocols together with Session Initiation Protocol (SIP) and Media Gateway control Protocol (MGCP), which ensures compatibility with a range of VoIP name servers, including Nortel conversation Server (CS) 2000 and Adaptive utility Engine platform, speeding deployment across an enterprise besides the fact that varied name servers are put in.

fashions in the LG-Nortel IP mobilephone 8800 series range from entry-level gadgets designed for ease of use by employees, to extra subtle fashions that present points akin to multiline help, a cell listing reveal and Bluetooth instant headset guide.

In connected news, LG-Nortel announced that the IP mobile 8800 series has been chosen through Cypress Communications, a provider of managed telephony and hosted unified communications solutions.

Nortel Launches New VoIP Softswitch | killexams.com Real Questions and Pass4sure dumps

Communications know-how company, Nortel, has announced the Nortel conversation Server 1500 (CS 1500), a second era VoIP softswitch that will allow small and medium-sized wireline provider suppliers to satisfy growing subscriber demand for local and lengthy-distance voice and next era features.

The CS 1500 core handle, gateways and operations, administration and upkeep interfaces are all housed in a single chassis solution. provider suppliers can installation the change for conclusion-workplace (classification 5) and access Tandem (type 4) functions to replace an present switch or as an addition to the network.

The entertaining component about the VoIP softswitch is that it makes it possible for service suppliers to leverage their present investment and infrastructure through providing compatibility with legacy networks. The change presents full range of legacy network interfaces and regulatory necessities, including E-911 and CALEA.

Making the announcement, Alan Stoddard, GM, service Multimedia Networks, Nortel noted, “Nortel is leveraging its VoIP management to support service providers seamlessly evolve their networks to a new economical packet infrastructure able to delivering the superior communications services, subscribers are traumatic.”

The product is complemented by a collection of Nortel global capabilities for CS 1500 to aid the whole network lifecycle, including engineering and installing, protection evaluation, integration and acceptance, assisted on-web page or remote network operations, and on-going renovation.

in line with Bettina Tratz-Ryan, research Director, Gartner, “VoIP is an anticipated and essentially required development of all communication networks, and the focus is on no longer simplest how to make it healthy within an evolving network and service ambiance, but also how to make it ecocnomic. To this conclusion, service providers are turning to softswitch architecture emigrate their networks to IP and carry scalable, multimedia functions to conclusion clients that generate new revenues from day one.”

The CS 1500 is scheduled for field trials beginning fourth quarter 2006 and generic availability in chosen global markets all over the primary quarter of 2007.

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A web server for comparative analysis of single-cell RNA-seq data | killexams.com real questions and Pass4sure dumps

Data collection and preprocessing

We selected a mouse gene set of interest based on the NCBI Consensus CDS (CCDS) which contains 20,499 distinct genes. For genes with multiple isoforms, we consolidate all available coding regions (Supporting Methods). To search for scRNA-seq datasets, we queried the NCBI Gene Expression Omnibus (GEO) and the ArrayExpress database for mouse single-cell RNA-seq series (See Supporting Methods for the queries we used). We then download metadata for each series returned in this query and parse this metadata to identify the distinct samples that comprise each series. We examined the metadata for each sample (e.g., library strategy, library source, data processing) and exclude any samples that do not contain scRNA-seq data.

We next attempted to download each study’s raw RNA-seq reads and for those studies for which these data are available, we developed a pipeline that uniformly processed scRNA-seq data. We use the reference mouse genome from the UCSC genome browser17 (build mm10), and align RNA-seq reads with HISAT218 version 2.1.0. We align reads as single-end or paired-end as appropriate, and discard samples for which fewer than 40% of reads align to coding regions. We represent gene expression using RPKM. See Supplementary Fig. 3 for a histogram of read counts per series.

Labeling using Cell Ontology terms

We use the Cell Ontology (CL)19 available from http://obofoundry.org/ontology/cl.html to identify the specific cell types which are represented in our GEO query results. We parsed the ontology terms into a directed acyclic graph structure, adding edges between terms for “is_a” and “part_of” relationships. Note that this choice of edge direction means that all edges point toward the root nodes in the ontology.

We use the name and any available synonyms for each ontology term to automatically identify the matching terms for each sample of interest (Supporting Methods). This produces a set of ontology term hits for each sample. We filter these ontology term hits by excluding any terms that are descendants of any other selected terms (e.g., term CL:0000000 “cell” matches many studies), producing a set of “specific” ontology terms for each sample—for any two nodes u and v in such a set, neither u nor v is a descendant of the other in the ontology.

Dimensionality reduction

Most current analysis methods for scRNA-seq data use some form of dimensionality reduction to visualize and analyze the data, most notably PCA and similar methods20,21 and t-SNE22. Past work has shown that while such methods are useful, supervised methods for dimensionality reduction may improve the ability to accurately represent different cell types10.

Using neural networks for dimensionality reduction has been shown to work well as a supervised technique to learn compact, discriminative representations of data23. The original, unreduced dimensions form the input layer to a neural network, where each dimension is an input unit. After training the model towards a particular objective (such as classification), the last hidden layer, which is typically much smaller in the number of units than the input layer, may be taken as a reduced dimensionality representation of the data. These learned features are referred to as neural embeddings in the literature, and here we tested a number of different neural network architectures which either explicitly optimize these neural embeddings (for example, siamese24 and triplet networks12) or those that only optimize the label accuracy. All neural networks we used were implemented in Python using the Keras API25.

Neural network architectures

Prior work showed that sparsely connected NN architectures based on protein interaction data can be more effective in determining cell types when compared to dense networks10. Here we further studied other NN networks architectures and compared their performance to the PPI and dense networks. First, we looked at another method to group genes based on the Gene Ontology (GO)26. To construct a hierarchical neural network architecture that mirrors the structure of GO, we associate input genes with GO nodes. Multiple genes are associated (and connected to) the same node. We use this grouping of the input genes as the first hidden layer of a neural network. Nodes in the next hidden layer will be constructed from GO nodes that are descendants of nodes in the prior layer. We continue this process until the last hidden layer has the desired number of nodes (the size of our reduced dimension). The final result is the network depicted in Supplementary Fig. 13. See also Supporting Methods.

Siamese architectures trained with contrastive loss

The NNs discussed above indirectly optimize the neural embedding layer by optimizing a classification target function (correct assignment of scRNA-seq data to cell types). A number of NN architectures have been proposed to explicitly optimize the embedding itself. For example, siamese neural networks11,24 (Supplementary Fig. 10) consist of two identical twin subnetworks which share the same weights. The outputs of both subnetworks are connected to a conjoined layer (sometimes referred to as the distance layer) which directly calculates a distance between the embeddings in the last layers of the twin networks. The input to a siamese network is a pair of data points and the output which is optimized is whether they are similar (same cell type) or not. The loss is computed on the output of the distance layer, and heavily penalizes large distances between items from the same class, while at the same time penalizing small distances between items from different class. Specifically, the network optimizes the following loss function:

$${\mathrm{Contrastive}}\,{\mathrm{loss}} = \mathop {\sum}\limits_{i = 1}^P \left( {Y^i} \right)L_s\left( {D^i} \right) + \left( {1 - Y^i} \right)L_d\left( {D^i} \right)$$


$$\begin{array}{l}{\mathrm{Where:}}\\ \quad \quad \,\,{P}\,{\mathrm{is}}\,{\mathrm{the}}\,{\mathrm{set}}\,{\mathrm{of}}\,{\mathrm{all}}\,{\mathrm{training}}\,{\mathrm{examples}}\,\left({{\mathrm{pairs}}\,{\mathrm{of}}\,{\mathrm{data}}\,{\mathrm{points}}} \right)\\ \quad \quad \,\,{Y}\,{\mathrm{is}}\,{\mathrm{the}}\,{\mathrm{corresponding}}\,{\mathrm{label}}\,{\mathrm{for}}\,{\mathrm{each}}\,{\mathrm{pair}}\,\left( {{\mathrm{1}}\,{\mathrm{indicates}}\,{\mathrm{that}}} \right.\\ \quad \quad \,\,\,\,{\mathrm{the}}\,{\mathrm{pair}}\,{\mathrm{belong}}\,{\mathrm{to}}\,{\mathrm{the}}\,{\mathrm{same}}\,{\mathrm{class}},{\mathrm{0}}\,{\mathrm{indicates}}\,{\mathrm{that}}\,{\mathrm{each}}\\ \quad \quad \, \, \left. {\, \, {\mathrm{sample}}\, {\mathrm{in}}\, {\mathrm{the}}\, {\mathrm{pair}}\, {\mathrm{come}}\, {\mathrm{from}}\, {\mathrm{different}}\, {\mathrm{classes}}} \right)\\ \quad \quad \, \, {D}\, {\mathrm{is}}\, {\mathrm{the}}\, {\mathrm{Euclidean}}\,{\mathrm{distance}}\, {\mathrm{between}}\, {\mathrm{the}}\, {\mathrm{points}}\, {\mathrm{in}}\,{\mathrm{the}}\,{\mathrm{pair}}\\ \quad \quad \, \, \, \, {\mathrm{computed}}\,{\mathrm{by}}\, {\mathrm{the}}\, {\mathrm{network}}\end{array}$$

$${L}_{s}\left( {D} \right) = \frac{1}{2}(D)^2$$


$$\begin{array}{l}{L}_{d}\left( {D} \right) = \frac{1}{2}\left( {{\mathrm{max}}\{ 0,m - D\} } \right)^2\\ \quad \quad \, \, {\mathrm{Where}}\,m\,\mathrm{is}\,\mathrm{a}\,\mathrm{margin}\,\mathrm{hyperparameter,}\,\mathrm{usually}\,\mathrm{set}\,\mathrm{to}\,1\end{array}$$


Following the same motivations as siamese networks, triplet networks also seek to learn an optimal embedding but do so by looking at three samples at a time instead of just two as in a siamese network. The triplet loss used by Schroff et al.12 considers a point (anchor), a second point of the same class as the anchor (positive), and a third point of a different class (negative). See Supporting Methods for details.

Training and testing of neural embedding models

We conduct supervised training of our neural embedding models using stochastic gradient descent. Although our processed dataset contains many cells, each with a set of labels, we train on a subset of “high confidence” cells to account for any label noise that may have occurred in our automatic term matching process. This is done by only keeping terms that have at least 75 cells mapping to them, and then only keeping cells with a single mapping term. This led to a training set of 21,704 cells from the data we processed ourselves (36,473 cells when combined with author-processed data). We experimented with tanh, sigmoid, and ReLU activations, and found that tanh performed the best. ReLU activation is useful for helping deeper networks converge by preventing the vanishing gradient problem, but here our networks only have a few hidden layers, so the advantage of ReLU is less clear. We also experimented with different learning rates, momentums, and input normalizations (see web server for full results).

Since our goal is to optimize a discriminative embedding, we test the quality of our neural embeddings with retrieval testing, which is similar to the task of cell-type inference. In retrieval testing, we query a cell (represented by the neural embedding of its gene expression vector) against a large database of other cells (which are also represented by their embeddings) to find the query’s nearest neighbors in the database.

Accounting for batch effects is a central issue in studies which integrate data from many different studies and experimental labs27,28. Here, we adopt a careful training and evaluation strategy in order to account for batch effects. We separate the studies for each cell type when training and testing so that the test set is completely independent of the training set. We find all cell types which come from more than one study, and hold out a complete study for each such cell type to be a part of the test set (sometimes referred to as the “query” set in the context of information retrieval). Cell types that do not exist in more than one study are all kept in the training set. For our integrated dataset, our training set contained 45 cell types, while our query set was a subset of 26 of the training cell types. After training the model using the training set, the training set can then be used as the database in retrieval testing.

Evaluation of classification and embeddings

In both training and evaluation of our neural embedding models, we are constantly faced with the question of how similar two cell types are. A rigid (binary) distinction between cell types is not appropriate since “neuron”, “hippocampus”, and “brain” are all related cell types, and a model that groups these cell types together should not be penalized as much as a model that groups completely unrelated cell types together. We have thus extended the NN learning and evaluation methods to incorporate cell type similarity when learning and testing the models. See Supporting Methods for details on how these are used and how they are obtained.

Differential expression for cell types

We use the automated scRNA-seq annotations we recovered to identify a set of differentially expressed genes for each cell type. Unlike prior methods that often compare two specific scRNA-seq datasets, or use data from a single lab, our integrated approach allows for a much more powerful analysis. Specifically, we can both focus on genes that are present in multiple datasets (and so do not represent specific data generation biases) and those that are unique in the context of the ontology graph (i.e., for two brain related types, find genes that distinguish them rather than just distinguishing brain vs. all others).

Our strategy, presented in Supplementary Algorithm 1, is DE-method agnostic, meaning that we can utilize any of the various DE tools that exist. In practice we have used Single-Cell Differential Expression (SCDE) here29. For this, we used read counts rather than RPKM, as SCDE requires count data as input. This method builds an error model for each cell in the data, where the model is a mixture between a negative binomial and a Poisson (for dropout events) distribution, and then uses these error models to identify differentially expressed genes. We also tried another method, limma with the voom transformation30, but the list of DE genes returned was too long for meaningful analysis (many of the reported DE genes had the same p-value). Results of our comparison of SCDE and limma-voom are shown in Supplementary Table 4.

Another key aspect of our strategy is the use of meta-analysis of multiple DE experiments. The algorithm attempts to make the best use of the integrated dataset by doing a separate DE experiment for each study that contains cells of a particular cell type, and then combines these results into a final list of DE genes for the cell type. See Supporting Methods for the details of this meta-analysis.

Large-scale query and retrieval

To enable users to compare new scRNA-seq data to the public data we have processed, and to determine the composition of cell types in such samples, we developed a web application. Users download a software package available on the website to process SRA/FASTQ files. The software implements a pipeline that generates RPKM values for the list of genes used in our database and can work on a PC or a server (Supporting Methods).

Once the user processes their data, the data are uploaded to the server and compared to all studies stored in the database. For this, we first use the NN to reduce the dimensions of each of the input profiles and then use approximate nearest neighbor approaches to match these to the data we have pre-processed as we discuss below.

Since the number of unique scRNA-seq expression vectors we store is large, an exact solution obtained by a linear scan of the dataset for the nearest neighbor cell types would be too slow. To enable efficient searches, we benchmarked three approximate nearest neighbor libraries: NMSLib31, ANNoY (https://github.com/spotify/annoy), and FALCONN32. Benchmarking revealed that NMSLib was the fastest method (Supplementary Table 3). NMSLib supports optimized implementations for cosine similarity and L2-distance based nearest neighbor retrieval. The indexing involves creation of hierarchical layers of proximity graphs. Hyperparameters for index building and query runtime were tuned to trade-off a high accuracy with reduced retrieval time. For NMSLib, these were: M = 10, efConstruction = 500, efSearch = 100, space = “cosinesimil”, method = “hnsw”, data_type = nmslib.DataType.DENSE_VECTOR, dtype = nmslib.DistType.FLOAT. Time taken to create the index: 2.6830639410000003 secs. Hyperparameters tuned for the ANNOY library were: number of trees = 50, search_k_var = 3000. Time taken to create the index: 1.3495307050000065 secs. For FALCONN, a routine to compute and set the hyperparameters at optimal values was used. This calibrates K (number of hash functions) and last_cp_dimensions. Time taken to create the index: 0.12065599400011706 s.

Visualizing query results

We use the approximate nearest neighbors results to compute a similarity measure of each query cell to each ontology term. This is done by identifying the 100 nearest neighbors for each cell and determining the fraction of these matches that belong to a specific cell type. This generates a matrix of similarity measure entries for all query cells against all cell types which is presented as a hierarchical clustering heatmap (Fig. 5a). All visualizations are based on this matrix.

For each query cell qi and nearest neighbor nk, we calculate the similarity score as:

$$s_{n_k}^{q_i} = 1{\mathrm{/}}\left( {1 + D\left( {q_i,n_k} \right)} \right)$$

Where k ∈ [1, 100] and D is the euclidean distance function.

We sum (over the nearest neighbors) the similarities to a specific cell type ct to obtain a cumulative similarity score of the query to that cell-type:

$$S_{ct}^{q_i} = \mathop {\sum}\limits_k \left( {s_{n_k}^{q_i} \ast 1_{ct}\left( {n_k} \right)} \right)$$


$$1_{ct}\left( {n_k} \right) = \left\{ {\begin{array}{*{20}{l}} 1 \hfill & {{\mathrm{if}}\,n_k\,{\mathrm{is}}\,{\mathrm{cell}}\,{\mathrm{type}}\,ct} \hfill \\ 0 \hfill & {{\mathrm{otherwise}}}\hfill \end{array}} \right.$$

Thus, we obtain for each query a vector of similarity scores against all cell types. Finally, we normalize the vector such that the cell-type-specific cumulative similarities sum to 1. Each normalized vector forms a row in the hierarchical heatmap.

We also perform further dimensionality reduction of the query via PCA to obtain a 2D nearest-neighbor style visualization against all cell types in the database and generate the ontology subgraph that matches the input cells. Users can click on any of the nodes in that graph to view the cell type associated with it, DE genes related to this cell type, and their expression in the query cells.

In addition to matching cells based on the NN reduced values, we also provide users with the list of experiments in our database that contain cells that are most similar to a subset of uploaded cells the user selects. This provides another layer of analysis beyond the automated (though limited) ontology matching that is based on the cell types extracted for the nearest neighbors.

Finally, users can obtain summary information about cell-type distribution in their uploaded cells and can find the set of cells matched to any of the cell types in our database.

Code availability

Code for our preprocessing (alignment/quantification) pipeline is available at https://github.com/mruffalo/sc-rna-seq-pipeline. Code for training and evaluation of our neural network models are available at https://github.com/AmirAlavi/scrna_nn. Code for our differential expression analysis to find cell-type-specific genes is available at https://github.com/AmirAlavi/single_cell_deg.

Sudden Windows 10 licence downgrades to forced Xcode upgrades: The week at Microsoft | killexams.com real questions and Pass4sure dumps

Roundup Aside from the hoo-ha around Windows 10 licences suddenly being downgraded amid an Insider build update, last week brought Row-Level Security to Azure warehouses, Alexa's tanks rolled onto Cortana's lawn and Microsoft prepared to drop support for old versions of Xcode.

Row-Level Security in Azure SQL Data Warehouse

DBAs might have been forgiven for thinking this was already a thing. After all, SQL Server 2016 had already introduced Row-Level Security (RLS) to govern which users have access to which data without the client application being any the wiser. Azure's own Azure SQL Database had also gained the feature in 2015.

Worry no more, warehouse fans, RLS is now generally available for Azure SQL Data Warehouse, meaning that it is now no longer necessary to manually filter out rows via views or, worse, rules in the application logic. Assuming you've got your SQL or Azure Active Directory roles set up in a logical fashion and not just stuffed every user into an admin group to deal with a mystery rights bug.

Microsoft reckons the functionality will be useful in scenarios such as a healthcare provider allowing nurses to only see the records of their patients, or a multi-tenant application enforcing logical separation of data.

Alas, only filter predicates are supported – so the functionality will only filter SELECT, UPDATE and DELETE operations to exclude rows that do not satisfy the predicate. Block predicates "will be released soon", according to Microsoft, limiting the utility of the functionality somewhat.

Alexa creeps into the Windows 10 store

Already on some PCs from Lenovo, Amazon's vaguely sinister personal assistant became available to all Windows 10 users this week, further ramping up the pressure on Microsoft's homegrown helper, Cortana.

Cortana was already reeling a little from the loss of the VP responsible for the unloved technology and now faces competition from a standalone Alexa app.

Admittedly, the level of Windows 10 integration is far deeper for Cortana. However, for PCs with "compatible hands-free" access, Alexa can be woken with a keyword and will then behave just as if your PC had turned into, er, an Echo.

For users not lucky enough to have compatible hardware, a click of a button in the Alexa app is sufficient to start the Amazon assistant listening.

The majority of Alexa's skills and features are supported, giving access to smart home devices as well as the shopping list that the retailer would dearly love you to use. PC-specific functions are, alas, not supported, nor are video, communications or Spotify. Amazon does, however, say that functions aimed at PC users will added in 2019.

The long-awaited integration between Cortana and Alexa finally became available in August. That will become less relevant as Amazon extends its tentacles further into the Windows world. At least as far as UK, US and German users are concerned.

Crazy about Kaizala

Almost lost in the ongoing furore around Microsoft's Skype messaging platform, the software giant's WhatsApp-alike Kaizala quietly announced it was getting ready to go global.

The app allows users to sign in with just a phone number (sound familiar?) and can scale to groups of up to one million users. With Office 365, users can set up surveys, polls and workflows and assess the results with the likes of Power BI.

The platform was kicked off back in 2016 as a garage project before hitting preview in 2017's Future Decoded event in India. It's now available in 28 markets as part of Office 365 or standalone (or via the Google and Apple app stores) but you'll need the Pro version to do anything useful such as creating public groups.

The Pro version is due to start being rolled out worldwide "over time", giving Microsoft users yet another communications platform to add to the likes of Skype and Teams.

Elderly Xcode banished from Visual Studio App Center

Microsoft is yanking support for older flavours of Xcode 8 from its continuous integration framework, Visual Studio App Center. Xcode 8 first made an appearance back in 2016 and Microsoft is allowing version 8.3.3 to linger past the announced 28 November cut-off date.

Since the platform supports Xcode 10 (and Microsoft made quite the fuss about launch-day support for iOS 12) Redmond reckons it is time for the less-than-1-per-cent of the users of its pipeline still clinging to the old days to make the move.

And if programmers don't take the hint, kind old Microsoft will automatically bump the version to 8.3.3 for them. ®

Sponsored: Becoming a Pragmatic Security Leader

Cray launches new supercomputers that can run AI workloads more effectively | killexams.com real questions and Pass4sure dumps

Two new cluster supercomputers from Cray offer accelerator-optimized solutions for machine learning and deep learning applications.


Image: Cray

Two new cluster supercomputers unveiled this week will help enterprises work with demanding artificial intelligence (AI) workloads. The Cray CS-Storm 500GT and the Cray CS-Storm 500NX offer accelerator-optimized solutions for running machine learning and deep learning applications, according to a press release.

Cray said that its new computer systems "are designed for organizations looking for the fastest path to new discoveries, a building block approach to scalability, and the assurance of collaborating with a trusted partner with a long history of designing and deploying tightly-integrated, highly-scalable systems," according to the press release. The machines feature NVIDIA Tesla GPU accelerators, and give customers a larger range of options for computational and data-intensive applications.

More about artificial intelligence

"Customer demand for AI-capable infrastructure is growing quickly, and the introduction of our new CS-Storm systems will give our customers a powerful solution for tackling a broad range of deep learning and machine learning workloads at scale with the power of a Cray supercomputer," said Fred Kohout, Cray's senior vice president of products and chief marketing officer, in the press release. "The exponential growth of data sizes, coupled with the need for faster time-to-solutions in AI, dictates the need for a highly-scalable and tuned infrastructure."

SEE: Video: The world's most powerful supercomputers

The CS-Storm machines provide up to 187 tera operations per second (TOPS) per node, 2,618 TOPS per rack for machine learning application performance, and up to 658 double precision TFLOPS per rack for HPC application performance, according to the release. The machines are delivered as fully integrated cluster computers, complete with the Cray Programming Environment, Cray Sonexion scale out storage, and full cluster systems management.

The Cray CS-Storm 500GT can support as many as 10 NVIDIA Tesla P40 or P100 PCIe accelerators, using balanced or single-root configurations for CPU-to-GPU communications. The CS-Storm 500NX includes support for eight Tesla P100 SXM2 accelerators, and uses the NVIDIA NVLink high speed interconnect for GPU-to-GPU communications.

"Early adopters of big data analytics and AI have learned a painful lesson as they have struggled to scale their applications and keep pace with data growth and use more sophisticated models," said Shahin Khan, founding partner at OrionX Research, in the press release. "You must have the right systems from the beginning to be able to scale, otherwise inefficiencies accumulate and multiply."

The 3 big takeaways for TechRepublic readers

1. This week, Cray released two new cluster supercomputers that offer accelerator-optimized solutions for running machine learning and deep learning applications.

2. The computer systems are designed for enterprises looking to scale up their AI work.

3. The machines also feature NVIDIA Tesla GPU accelerators, and give customers a larger range of options for computational and data-intensive applications.

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Nortel 920-324 Exam (Communication Server (CS) Rls. 4.0 Database Administrator) Detailed Information


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