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

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Test Code : 920-324
Test appellation : Communication Server (CS) Rls. 4.0 Database Administrator
Vendor appellation : Nortel
: 58 actual Questions

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Nortel Communication Server (CS) Rls.

Microsoft and Nortel gain Unified Communications available to agencies far and wide | actual 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 diminutive and midsize agencies (SMBs), extending benefits prior to now available best to gigantic businesses. The hosted retort is based on the Nortel Communications Server 2000 (CS 2000), an internet Protocol multimedia softswitch, and the Microsoft retort 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 quicken 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 proffer SMBs new, efficacious unified communications that coalesce real-time communications, equivalent to voice, with Microsoft’s collaboration capabilities, corresponding to click-to-call, click-to-conference, overlooked appellation electronic mail messages, mobility and telephony presence. These facets any drudgery seamlessly with the Microsoft office portfolio and the Microsoft workplace Communicator customer to carry a unique journey to the End person. This current retort moreover enables SMBs to odds from unified communications while not having to purchase, set up, manage and maintain additional network infrastructure and involved 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 any sizes. For carrier providers, this retort presents current 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 their provider company purchasers,” referred to Steve Zimba, managing director, global Telecom company, Microsoft. “The totally hosted unified communications retort gives opportunities for extended revenue and helps carrier providers deliver the related drudgery style to SMBs, offering the capacity to talk virtually every time, anyplace and on any equipment.”

At NXTcomm08, Microsoft and Nortel will exhibit the hosted unified communications retort and panoply its value in bettering commerce agility. NXTcomm08 attendees can gleam additional information with the aid of touring the Microsoft booths SL5916 and SL5923 and the Nortel sales space SL3516. more information can moreover subsist discovered at

About Nortel

Nortel is a identified leader in delivering communications capabilities that gain the engage of commerce Made simple a fact for their shoppers. Their 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 For the newest Nortel information, debate with

About Microsoft

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

word to editors: in case you are interested in viewing more information on Microsoft, gladden debate with the Microsoft internet web page at on Microsoft’s company tips pages. web links, mobile numbers and titles were redress at time of booklet, however can moreover since believe modified. For further advice, journalists and analysts can moreover contact Microsoft’s hasty Response team or other appropriate contacts listed at

LG-Nortel Unveils Line of desktop IP telephones | actual Questions and Pass4sure dumps

the brand current standards-compliant IP telephone 8800 collection with the aid of LG-Nortel, a three route 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 ambit of VoIP appellation servers, including Nortel conversation Server (CS) 2000 and Adaptive utility Engine platform, speeding deployment across an enterprise besides the fact that varied appellation servers are achieve in.

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

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

Nortel Launches current VoIP Softswitch | actual 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 diminutive and medium-sized wireline provider suppliers to answer growing subscriber require for local and lengthy-distance voice and next era features.

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

The entertaining component about the VoIP softswitch is that it makes it viable for service suppliers to leverage their present investment and infrastructure through providing compatibility with legacy networks. The change presents complete ambit 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 current 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 gross 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 progress of any communication networks, and the focus is on no longer simplest how to gain it robust within an evolving network and service ambiance, but moreover how to gain 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 current revenues from day one.”

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

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Communication Server (CS) Rls. 4.0 Database Administrator

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A web server for comparative analysis of single-cell RNA-seq data | actual 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 sunder genes. For genes with multiple isoforms, they consolidate any available coding regions (Supporting Methods). To search for scRNA-seq datasets, they queried the NCBI Gene Expression Omnibus (GEO) and the ArrayExpress database for mouse single-cell RNA-seq sequence (See Supporting Methods for the queries they used). They then download metadata for each sequence returned in this query and parse this metadata to identify the sunder samples that comprise each series. They examined the metadata for each sample (e.g., library strategy, library source, data processing) and exclude any samples that Do not hold 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, they developed a pipeline that uniformly processed scRNA-seq data. They utilize the reference mouse genome from the UCSC genome browser17 (build mm10), and align RNA-seq reads with HISAT218 version 2.1.0. They align reads as single-end or paired-end as appropriate, and discard samples for which fewer than 40% of reads align to coding regions. They limn gene expression using RPKM. see Supplementary Fig. 3 for a histogram of read counts per series.

Labeling using Cell Ontology terms

We utilize the Cell Ontology (CL)19 available from to identify the specific cell types which are represented in their GEO query results. They parsed the ontology terms into a directed acyclic graph structure, adding edges between terms for “is_a” and “part_of” relationships. Note that this preference of edge direction means that any edges point toward the root nodes in the ontology.

We utilize the appellation 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. They 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 utilize some profile of dimensionality reduction to visualize and anatomize the data, most notably PCA and similar methods20,21 and t-SNE22. Past drudgery has shown that while such methods are useful, supervised methods for dimensionality reduction may better the capacity to accurately limn different cell types10.

Using neural networks for dimensionality reduction has been shown to drudgery well as a supervised technique to learn compact, discriminative representations of data23. The original, unreduced dimensions profile 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 ultimate hidden layer, which is typically much smaller in the number of units than the input layer, may subsist taken as a reduced dimensionality representation of the data. These learned features are referred to as neural embeddings in the literature, and here they 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. any neural networks they used were implemented in Python using the Keras API25.

Neural network architectures

Prior drudgery showed that sparsely connected NN architectures based on protein interaction data can subsist more efficacious in determining cell types when compared to dense networks10. Here they further studied other NN networks architectures and compared their performance to the PPI and dense networks. First, they looked at another manner to group genes based on the Gene Ontology (GO)26. To construct a hierarchical neural network architecture that mirrors the structure of GO, they associate input genes with travel nodes. Multiple genes are associated (and connected to) the same node. They utilize this grouping of the input genes as the first hidden layer of a neural network. Nodes in the next hidden layer will subsist constructed from travel nodes that are descendants of nodes in the prior layer. They continue this process until the ultimate hidden layer has the desired number of nodes (the size of their 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 office (correct assignment of scRNA-seq data to cell types). A number of NN architectures believe been proposed to explicitly optimize the embedding itself. For example, siamese neural networks11,24 (Supplementary Fig. 10) consist of two identical twin subnetworks which participate 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 ultimate 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 big distances between items from the same class, while at the same time penalizing diminutive 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 moreover quest 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 their neural embedding models using stochastic gradient descent. Although their processed dataset contains many cells, each with a set of labels, they train on a subset of “high confidence” cells to account for any label babel that may believe occurred in their automatic term matching process. This is done by only keeping terms that believe 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 they processed ourselves (36,473 cells when combined with author-processed data). They experimented with tanh, sigmoid, and ReLU activations, and organize that tanh performed the best. ReLU activation is useful for helping deeper networks converge by preventing the vanishing gradient problem, but here their networks only believe a few hidden layers, so the odds of ReLU is less clear. They moreover experimented with different learning rates, momentums, and input normalizations (see web server for complete results).

Since their goal is to optimize a discriminative embedding, they test the property of their neural embeddings with retrieval testing, which is similar to the stint of cell-type inference. In retrieval testing, they query a cell (represented by the neural embedding of its gene expression vector) against a big database of other cells (which are moreover 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, they adopt a watchful training and evaluation strategy in order to account for batch effects. They sunder the studies for each cell kind when training and testing so that the test set is completely independent of the training set. They find any cell types which Come from more than one study, and hold out a complete study for each such cell kind to subsist a participate 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 any kept in the training set. For their integrated dataset, their training set contained 45 cell types, while their 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 subsist used as the database in retrieval testing.

Evaluation of classification and embeddings

In both training and evaluation of their neural embedding models, they 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 any related cell types, and a model that groups these cell types together should not subsist penalized as much as a model that groups completely unrelated cell types together. They believe thus extended the NN learning and evaluation methods to incorporate cell kind 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 utilize the automated scRNA-seq annotations they 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 utilize data from a single lab, their integrated approach allows for a much more powerful analysis. Specifically, they can both focus on genes that are present in multiple datasets (and so Do not limn 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. any others).

Our strategy, presented in Supplementary Algorithm 1, is DE-method agnostic, sense that they can utilize any of the various DE tools that exist. In drill they believe used Single-Cell Differential Expression (SCDE) here29. For this, they used read counts rather than RPKM, as SCDE requires signify data as input. This manner builds an oversight model for each cell in the data, where the model is a composite between a negative binomial and a Poisson (for dropout events) distribution, and then uses these oversight models to identify differentially expressed genes. They moreover 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 their comparison of SCDE and limma-voom are shown in Supplementary Table 4.

Another key aspect of their strategy is the utilize of meta-analysis of multiple DE experiments. The algorithm attempts to gain the best utilize of the integrated dataset by doing a sunder 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 current scRNA-seq data to the public data they believe processed, and to determine the composition of cell types in such samples, they 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 their database and can drudgery on a PC or a server (Supporting Methods).

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

Since the number of unique scRNA-seq expression vectors they store is large, an exact solution obtained by a linear scan of the dataset for the nearest neighbor cell types would subsist too slow. To enable efficient searches, they benchmarked three approximate nearest neighbor libraries: NMSLib31, vex (, and FALCONN32. Benchmarking revealed that NMSLib was the fastest manner (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 structure and query runtime were tuned to trade-off a tall 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 vex 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 utilize 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 any query cells against any cell types which is presented as a hierarchical clustering heatmap (Fig. 5a). any visualizations are based on this matrix.

For each query cell qi and nearest neighbor nk, they reckon 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 kind 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, they obtain for each query a vector of similarity scores against any cell types. Finally, they 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 moreover perform further dimensionality reduction of the query via PCA to obtain a 2D nearest-neighbor style visualization against any 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, they moreover provide users with the list of experiments in their database that hold 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 their database.

Code availability

Code for their preprocessing (alignment/quantification) pipeline is available at Code for training and evaluation of their neural network models are available at Code for their differential expression analysis to find cell-type-specific genes is available at

Sudden Windows 10 licence downgrades to forced Xcode upgrades: The week at Microsoft | actual questions and Pass4sure dumps

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

Row-Level Security in Azure SQL Data Warehouse

DBAs might believe 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 believe access to which data without the client application being any the wiser. Azure's own Azure SQL Database had moreover gained the feature in 2015.

Worry no more, warehouse fans, RLS is now generally available for Azure SQL Data Warehouse, sense 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 dynamic Directory roles set up in a analytic style and not just stuffed every user into an admin group to deal with a mystery rights bug.

Microsoft reckons the functionality will subsist useful in scenarios such as a healthcare provider allowing nurses to only see the records of their patients, or a multi-tenant application enforcing analytic 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 answer the predicate. screen predicates "will subsist 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 malign personal assistant became available to any Windows 10 users this week, further ramping up the pressure on Microsoft's homegrown helper, Cortana.

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

Admittedly, the flat of Windows 10 integration is far deeper for Cortana. However, for PCs with "compatible hands-free" access, Alexa can subsist 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 believe compatible hardware, a click of a button in the Alexa app is adequate 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 worship you to use. PC-specific functions are, alas, not supported, nor are video, communications or Spotify. Amazon does, however, bid 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 apropos 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 travel global.

The app allows users to symptom 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 participate 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 noiseless clinging to the old-fashioned days to gain the move.

And if programmers don't prefer the hint, benign old-fashioned Microsoft will automatically bump the version to 8.3.3 for them. ®

Sponsored: Becoming a Pragmatic Security Leader

Cray launches current supercomputers that can dash AI workloads more effectively | actual questions and Pass4sure dumps

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


Image: Cray

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

Cray said that its current computer systems "are designed for organizations looking for the fastest path to current discoveries, a structure screen 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 ambit of options for computational and data-intensive applications.

More about ersatz intelligence

"Customer require for AI-capable infrastructure is growing quickly, and the introduction of their current CS-Storm systems will give their customers a powerful solution for tackling a broad ambit 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 complete 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 tall quicken interconnect for GPU-to-GPU communications.

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

The 3 colossal takeaways for TechRepublic readers

1. This week, Cray released two current cluster supercomputers that proffer 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 moreover feature NVIDIA Tesla GPU accelerators, and give customers a larger ambit of options for computational and data-intensive applications.

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