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There comes a degree where respectable ideas don't look to subsist satisfactory; conceivable, proven solutions must pick flight.

here's the region where cloud computing finds itself these days, certainly when the dialog turns towards inordinate efficiency computing (HPC).

The astronomical majority of businesses at the present time are leveraging the agility, can imbue benefits, and solace offered by way of the public cloud services mannequin. HPC amenities are additionally exploring the benefits of cloud, but most likely no longer for the same factors. Whereas more traditional corporations are most often looking to public cloud functions providers for backup and catastrophe recuperation options or the alternative to reduce storage prices by parking seldom-accessed records sets on much less expensive cloud storage, HPC amenities usually attend to subsist exploring the chances of accommodating spikes admired with the aid of dynamically adding and subtracting cloud-primarily based substances.

These every single sound fancy very satisfactory concepts, but making them work, with no stitch and reliably, has been a real problem. Cloud compute materials, in contrast to cloud storage, will also subsist extra costly than on-premises infrastructure. security is always a consideration. pitiable colossal HPC records units over networks can also subsist prohibitively gradual and cumbersome. and you requisite to maintain requisite to maintain purposes which can operate acceptably on cloud-primarily based supplies.

When off-web page cloud resources are integrated with on-premises computing, the outcomes is referred to as a hybrid cloud architecture. There is not any one usurp technique to assemble an HPC hybrid cloud computing cluster. each web site is distinct, and users Run distinctive workloads that maintain very different aid consumption patterns. as an instance, some deep discovering practicing consumes great amounts of information to provide a wee mannequin. Others are especially CPU intensive, or GPU intensive. The hybrid cluster solution truly carried out will of direction subsist pushed by means of the workloads HPC users want to run, however two hybrid architectures often upward propel to the desirable when IT architects tackle the challenges of constructing cloud cruise for HPC – the “stretch cluster” configuration and the “multi-cluster” configuration. To accommodate spikes widespread, companies working natural HPC environments are increasingly looking at hybrid clouds to supply the additional means when it’s really needed.  Stretch and multi-cluster hybrid cloud solutions can champion address this problem by using enabling dynamic entry to cloud resources.

A stretch cluster skill that you just lengthen or multiply your on-premises HPC compute cluster with further cloud-based mostly compute nodes, when vital. This architecture assumes that you maintain a compute cluster in a different vicinity – either on-premises or configured in a cloud. This structure is formally described as a single cluster stretched over a wide enviornment network (WAN) in order that compute nodes on the other belt or within the cloud communicate with a master scheduling host on the originating location. notwithstanding a satisfactory deal easier in theory than a multi-cluster, this skill that each one communique and coordination with the master scheduler occurs over the WAN, which can also subsist a source of added can imbue or decreased reliability.

A multi-cluster hybrid cloud configuration is a greater complex structure, since it provides a grasp scheduler working in the cloud. by means of together with this additional master scheduler within the cloud, the architecture eliminates much of the communication from cloud compute nodes to the on-premises master. the two grasp schedulers as a substitute trade project meta-facts in a “job forwarding” mannequin. in this mannequin, clients on-premises post application workloads to a queue on-premises, which in revolve forwards that workload to the cloud for execution. Upon job completion, the scheduler running in the cloud synchronizes the job popularity with the on-premises environment and a notification is equipped to the user.

The query turns into, what are the deployment, configuration, and optimization particulars for HPC stretch and multi-cluster hybrid cloud solutions? How execute I truly obtain this issue fly? A coincident announcement from IBM provides the solutions. In a crucial step along the pathway to possible, respectable, easy-to-use hybrid cloud options, IBM has developed and launched a deployment ebook that allows using IBM Spectrum LSF with Amazon net services (AWS). This LSF cloud deployment e engage builds on an current relationship with AWS. IBM is featuring potential, features, and management capabilities in an effort to give IBM Spectrum LSF clients quickly, bendy access to AWS offerings.

the recent deployment engage offers information for building a wide array of customizable stretch and multi-cloud IBM Spectrum LSF cluster configurations that permit clients to extra without problems and conveniently gain the agility, can imbue benefits, and convenience provided through cloud computing.

IBM Spectrum LSF is fraction of a comprehensive Suite of solutions supporting traditional HPC and inordinate throughput environments, in addition to astronomical facts, synthetic Intelligence, GPU, laptop learning, and containerized workloads, amongst many others. IBM Spectrum LSF, the core of the Suite, is a powerful workload administration platform for worrying, disbursed HPC environments. It gives an entire set of intelligent, policy-driven scheduling points that champion maximize utilization of compute infrastructure elements while optimizing software performance. LSF is the HPC workload management ordinary, with probably the most finished set of capabilities – from license scheduling and session scheduling to superior analytics.

IBM Spectrum LSF Suite comes in three variants and includes a few extra components corresponding to IBM Spectrum LSF useful resource Connector, which permits coverage-pushed cloud bursting to every single fundamental cloud functions, together with IBM Cloud, AWS, Google, and Azure. And now, IBM is providing variable spend licensing for IBM Spectrum LSF which capacity that LSF users should subsist in a position to Run the solution essentially any place, and pay best for what they use.

The cloud offers many merits, but constructing efficient hybrid cloud solutions isn’t so essential – previously. With the main-edge performance offered with the aid of IBM Spectrum LSF and the assist of the brand recent LSF deployment e engage for AWS, HPC facilities are a gross lot greater able to obtain cloud cruise for them.

study leading-side HPC and AI options on the IBM sales space next week at SC18 (booth #3433) in Dallas, Texas. Register here for technical briefings and consumer group classes.

read greater about dynamic hybrid cloud with IBM Spectrum LSF at the IBM IT Infrastructure weblog – IBM Spectrum LSF Goes Multicloud.

a more in-depth emerge on the deployment e engage and assisting substances for constructing IBM Spectrum LSF hybrid-cloud configurations with Amazon internet functions can also subsist discovered here.


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P2050-007 exam Dumps Source : IBM Optimization Technical Mastery Test v1

Test Code : P2050-007
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Vendor designation : IBM
: 30 real Questions

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All in all, killexams.com was a satisfactory way for me to prepare for this exam. I passed, but was a petite disappointed that now every single questions on the exam were 100% the same as what killexams.com gave me. Over 70% were the same and the rest was very similar - Im not certain if this is a satisfactory thing. I managed to pass, so I cogitate this counts as a satisfactory result. But keep in mind that even with killexams.com you quiet requisite to learn and spend your brain.


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Tips & tricks to certify P2050-007 exam with elevated scores.
hi! i am julia from spain. want to skip the P2050-007 exam. but. My English is very negative. The language is simple and contours are brief . No stitch in mugging. It helped me wrap up the training in 3 weeks and that i passed wilh 88% marks. now not capable of crack the books. long lines and arduous words obtain me sleepy. wished an smooth manual badly and ultimately located one with the killexams.com brain dumps. I were given every single query and solution . extraordinary, killexams! You made my day.


revel in assured via making ready P2050-007 dumps.
some precise men cant deliver an alteration to the worlds manner but they could only let you know whether or notyouve got been the simplest man who knew the way to execute that and that i want to subsist acknowledged on this global and obtain my personal designate and i maintain been so lame my complete manner but I understand now that I desired to accept a bypassin my P2050-007 and this can obtain me famed perhaps and yes i am short of glory however passing my A+ exams with killexams.com was my morning and night time glory.


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I am fortunate to inform that I maintain successfully passed the P2050-007 Exam. In this context I must admit that your Question Bank did befriend (if not fully) to tied over the exam as the questions asked in the exam were not fully covered by your question bank. HOwever I must congratulate your effort to obtain us technically sound with your s. Thanks to killexams.com for clearing my P2050-007 exam in first class.


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Unfriendly Skies: Predicting Flight Cancellations Using Weather Data, fraction 2 | killexams.com real questions and Pass4sure dumps

Ricardo Balduino and Tim Bohn

Early Flight, Creative Commons Introduction

As they described in fraction 1 of this series, their objective is to befriend forecast the probability of the cancellation of a flight between two of the ten U.S. airports most affected by weather conditions. They spend historical flights data and historical weather data to obtain predictions for upcoming flights.

Over the course of this four-part series, they spend different platforms to befriend us with those predictions. Here in fraction 2, they spend the IBM SPSS Modeler and APIs from The Weather Company.

Tools used in this spend case solution

IBM SPSS Modeler is designed to befriend learn patterns and trends in structured and unstructured data with an intuitive visual interface supported by advanced analytics. It provides a orbit of advanced algorithms and analysis techniques, including text analytics, entity analytics, decision management and optimization to deliver insights in near real-time. For this spend case, they used SPSS Modeler 18.1 to create a visual representation of the solution, or in SPSS terms, a stream. That’s right — not one line of code was written in the making of this blog.

We also used The Weather Company APIs to retrieve historical weather data for the ten airports over the year 2016. IBM SPSS Modeler supports calling the weather APIs from within a stream. That is accomplished by adding extensions to SPSS, available in the IBM SPSS Predictive Analytics resources page, a.k.a. Extensions Hub.

A proposed solution

In this blog, they propound one possible solution for this problem. It’s not meant to subsist the only or the best possible solution, or a production-level solution for that matter, but the discussion presented here covers the typical iterative process (described in the sections below) that helps us accumulate insights and refine the predictive model across iterations. They cheer the readers to try and forward up with different solutions, and provide us with your feedback for future blogs.

Business and data understanding

The first step of the iterative process includes understanding and gathering the data needed to train and test their model later.

Flights data — We gathered 2016 flights data from the US Bureau of Transportation Statistics website. The website allows us to export one month at a time, so they ended up with 12 csv (comma separated value) files. They used IBM SPSS Modeler to merge every single the csv files into one set and to select the ten airports in their scope. Some data clean-up and formatting was done to validate dates and hours for each flight, as seen in device 1.

Figure 1 — gathering and preparing flights data in IBM SPSS Modeler

Weather data — From the Extensions Hub, they added the TWCHistoricalGridded extension to SPSS Modeler, which made the extension available as a node in the tool. That node took a csv file listing the 10 airports latitude and longitude coordinates as input, and generated the historical hourly data for the entire year of 2016, for each airport location, as seen in device 2.

Figure 2 — gathering and preparing weather data in IBM SPSS Modeler

Combined flights and weather data — To each flight in the first data set, they added two recent columns: source and DEST, containing the respective airport codes. Next, flight data and the weather data were merged together. Note: the “stars” or SPSS super nodes in device 3 are placeholders for the diagrams in Figures 1 and 2 above.

Figure 3 — combining flights and weather data in IBM SPSS Modeler Data preparation, modeling, and evaluation

We iteratively performed the following steps until the desired model qualities were reached:

· Prepare data

· effect modeling

· Evaluate the model

· Repeat

Figure 4 shows the first and second iterations of their process in IBM SPSS Modeler.

Figure 4 — iterations: prepare data, Run models, evaluate — and execute it again First iteration

To start preparing the data, they used the combined flights and weather data from the previous step and performed some data cleanup (e.g. took supervision of null values). In order to better train the model later on, they filtered out rows where flight cancellations were not related to weather conditions (e.g. cancellations due to technical issues, security issues, etc.)

Figure 5 — imbalanced data establish in their input data set

This is an titillating spend case, and often a arduous one to solve, due to the imbalanced data it presents, as seen in device 5. By “imbalanced” they subsist substantive that there were far more non-cancelled flights in the historical data than cancelled ones. They will discuss how they dealt with imbalanced data in the following iteration.

Next, they defined which features were required as inputs to the model (such as flight date, hour, day of the week, source and destination airport codes, and weather conditions), and which one was the target to subsist generated by the model (i.e. forecast the cancellation status). They then partitioned the data into training and testing sets, using an 85/15 ratio.

The partitioned data was fed into an SPSS node called Auto Classifier. This node allowed us to Run multiple models at once and preview their outputs, such as the belt under the ROC curve, as seen in device 6.

Figure 6 — models output provided by the Auto Classifier node

That was a useful step in making an initial selection of a model for further refinement during subsequent iterations. They decided to spend the Random Trees model since the initial analysis showed it has the best belt under the curve as compared to the other models in the list.

Second iteration

During the second iteration, they addressed the skewedness of the original data. For that purpose, they chose one of the SPSS nodes called SMOTE (Synthetic Minority Over-sampling Technique). This node provides an advanced over-sampling algorithm that deals with imbalanced datasets, which helped their selected model travail more effectively.

Figure 7 — distribution of cancelled and non-cancelled flights after using SMOTE

In device 7, they notice a more balanced distribution between cancelled and non-cancelled flights after running the data through SMOTE.

As mentioned earlier, they picked the Random Trees model for this sample solution. This SPSS node provides a model for tree-based classification and prediction that is built on Classification and Regression Tree methodology. Due to its characteristics, this model is much less prostrate to overfitting, which gives a higher likelihood of repeating the same test results when you spend recent data, that is, data that was not fraction of the original training and testing data sets. Another odds of this method — in particular for their spend case — is its aptitude to handle imbalanced data.

Since in this spend case they are dealing with classification analysis, they used two common ways to evaluate the performance of the model: confusion matrix and ROC curve. One of the outputs of running the Random Trees model in SPSS is the confusion matrix seen in device 8. The table shows the precision achieved by the model during training.

Figure 8 — Confusion Matrix for cancelled vs. non-cancelled flights

In this case, the model’s precision was about 95% for predicting cancelled flights (true positives), and about 94% for predicting non-cancelled flights (true negatives). That means, the model was remedy most of the time, but also made wrong predictions about 4–5% of the time (false negatives and erroneous positives).

That was the precision given by the model using the training data set. This is also represented by the ROC curve on the left side of device 9. They can see, however, that the belt under the curve for the training data set was better than the belt under the curve for the testing data set (right side of device 9), which means that during testing, the model did not effect as well as during training (i.e. it presented a higher rate of errors, or higher rate of erroneous negatives and erroneous positives).

Figure 9 — ROC curves for the training and testing data sets

Nevertheless, they decided that the results were quiet satisfactory for the purposes of their discussion in this blog, and they stopped their iterations here. They cheer readers to further refine this model or even to spend other models that could resolve this spend case.

Deploying the model

Finally, they deployed the model as a rest API that developers can call from their applications. For that, they created a “deployment branch” in the SPSS stream. Then, they used the IBM Watson Machine Learning service available on IBM Bluemix here. They imported the SPSS stream into the Bluemix service, which generated a scoring endpoint (or URL) that application developers can call. Developers can also call The Weather Company APIs directly from their application code to retrieve the forecast data for the next day, week, and so on, in order to pass the required data to the scoring endpoint and obtain the prediction.

A typical scoring endpoint provided by the Watson Machine Learning service would search for fancy the URL shown below.

https://ibm-watson-ml.mybluemix.net/pm/v1/score/flights-cancellation?accesskey=<provided by WML service>

By passing the expected JSON carcass that includes the required inputs for scoring (such as the future flight data and forecast weather data), the scoring endpoint above returns if a given flight is likely to subsist cancelled or not. This is seen in device 10, which shows a call being made to the scoring endpoint — and its response — using an HTTP requester implement available in a web browser.

Figure 10 — actual request URL, JSON body, and response from scoring endpoint

Notice in the JSON response above that the deployed model predicted this particular flight from Newark to Chicago would subsist 88.8% likely to subsist cancelled, based on forecast weather conditions.

Conclusion

IBM SPSS Modeler is a powerful implement that helped us visually create a solution for this spend case without writing a single line of code. They were able to ensue an iterative process that helped us understand and prepare the data, then model and evaluate the solution, to finally deploy the model as an API for consumption by application developers.

Resources

The IBM SPSS stream and data used as the basis for this blog are available on GitHub. There you can also find instructions on how to download IBM SPSS Modeler, accept a key for The Weather Channel APIs, and much more.


The Week In Review: Design | killexams.com real questions and Pass4sure dumps

M&AIoT-focused memory chipmaker Adesto Technologies acquired S3 Semiconductors, a provider of mixed-signal and RF ASICs and IP. Based in Ireland, S3 Semiconductors was founded in 1986. S3 Semiconductors will become a trade unit of Adesto and will continue to operate under its current model in the $35 million deal. S3 Semiconductor’s parent company, S3 Group, will continue as a sunder company focused on smart medical devices.

ToolsCadence debuted a recent design-for-reliability implement for analog and mixed-signal IC design. The Legato Reliability Solution incorporates analog defect analysis and simulation to evaluate the aptitude to liquidate die with manufacturing defects and resulting test escapes that antecedent realm failures, as well as electro-thermal and aging analysis to prevent thermal overstress and forecast wear-out.

Aldec made several updates to its Riviera-PRO verification platform, adding a UVM Register Generator for automated register model creation. The UVM Register Generator supports input of IP-XACT component files and CSV files. Other additions embrace a Unit Linting duty that runs ALINT-PRO in the background and champion for the latest extensions to VHDL.

XJTAG uncorked free DFT software for users of Mentor’s Xpedition product to multiply the design for test and debug capabilities of the schematic capture and PCB design environment. XJTAG DFT lieutenant helps validate remedy JTAG chain connectivity by finding and repairing errors.

IPArasan Chip Systems uncorked its MIPI I3C Host Controller Interface (I3C HCI) Master IP core compliant to the recently released MIPI I3C HCI Specification 1.0. The IP kit includes a Linux software stack compatible with the I3C HCI Master IP Core, an I3C Hardware evolution Kit to enable prototyping and compliance testing of I3C products, and the I3C Slave IP Core.

Chips&Media launched its first computer vision IP, which implements a convolutional neural network capable of processing 4K resolution at 60 frames per second input in real-time for kick detection. The IP targets edge devices for automotive and surveillance applications.

Chipus taped out a set of its ultra-low power analog IP on SilTerra’s 0.18um I18L IoT Platform. The test chip includes a temperature sensor, RTC oscillators, 6MHz oscillators, and an ultra-low power bandgap reference.

StandardsThe Si2 Compact Model Coalition published the Open Model Interface, a C-language application programming interface that supports SPICE compact model extensions. OMI allows for simulation and analysis of physical effects such as self-heating and aging as well as design optimization. It is based on TMI2, the TSMC Model Interface, which was donated to Si2 by TSMC in 2014. So far, OMI supports BSIM, BSIM-CMG, HiSIM2, and BSIM-SOI.

The MIPI Alliance released MIPI Touch, a family of publicly available specifications for the design and implementation of feel applications, such as in smartphones. The effort ties together several MIPI specifications for touch, sensor, and software integration: MIPI feel Command Set (MIPI TCS) for writing device drivers across operating systems; MIPI feel Adaptation Layer for I3C (MIPI ALI3C) to translate feel commands for spend on the MIPI I3C protocol; MIPI I3C Host Controller Interface (MIPI I3C HCI), which enables feel commands and multiple data streams for advanced systems; and MIPI I3C v1.0, which connects an application processor to a feel sensor.

DealsRambus and GigaDevice formed a joint venture, Reliance Memory, in China to focus on evolution and commercialization of Resistive Ram (RRAM), a non-volatile memory that works by changing the resistance of materials when different voltages are applied. The joint venture has investment funding from THG Ventures, West apex Capital, Walden International, and Zhisland Capital.

Esperanto Technologies inked deals for both NetSpeed Systems’ network-on-chip IP and design environment and UltraSoC’s embedded analytics and debug IP for its Supercomputer-on-a-Chip for AI applications. The 7nm chip will integrate thousands of RISC-V compatible processors each with vector/tensor accelerators.

NetSpeed Systems and Northwest Logic are teaming up on a elevated throughput memory subsystem solution that uses NetSpeed’s interconnect IP and Northwest Logic’s HBM2 and GDDR6 memory controllers for customers designing SoCs for hyperscale and automotive applications. The companies will provide a common platform to extend NetSpeed’s dynamic-priority elevation approach to the memory controller so that system-level traffic-flow priorities are handled in an end-to-end solution.

NumbersIntel Capital placed investments in two firms: Movellus and SiFive. Movellus provides generator tools enabling automatic creation and implementation of PLL, DLL, and LDO digital IP and will spend the funds to accelerate market deployment and adoption of its technology. SiFive is a provider of RISC-V IP cores, SoCs, and evolution boards, as well a RISC-V-focused IP catalog.

Rambus released fiscal results for first quarter 2018 with revenue of $46.4 million. On a GAAP basis, there was a loss per participate of $0.36; on a non-GAAP basis there was a loss per participate of $0.10. Rambus changed to a recent reporting standard this year; under the previous reporting standard, revenue for Q1 2018 was $100.5 million, up 4% from the same quarter eventual year. GAAP income per participate for the quarter was $0.05, up 66.7% from $0.03 in Q1 2017; non-GAAP income per participate was $0.21, up 23.5% from $0.17. The company posted record revenue for its memory and interface IP cores.

Jesse Allen   (all posts)Jesse Allen is the erudition seat administrator and a senior editor at Semiconductor Engineering.

Cisco refused to participate in NSS Labs report on SD-WAN | killexams.com real questions and Pass4sure dumps

Cisco refused to activate the Viptela software-defined WAN product NSS Labs bought for testing, leaving the research...

firm with a noticeable pocket in its recent comparative report on SD-WAN vendors.

Cisco did not provide a intuition for refusing to activate the product NSS Labs had purchased for between $30,000 and $40,000, NSS Labs CEO Vikram Phatak said this week. "There was no intuition given other than, effectively, they didn't want to subsist tested (for the NSS Labs report)."

Cisco's action marked the first time a vendor had refused to revolve on a product NSS Labs had bought for evaluation, Phatak said. Cisco's Viptela team had initially told NSS Labs it would champion the test, which led the arduous to buy the product.

"That's a first for us, candidly," Phatak said. "And given Cisco's ethical rules and so on -- rules of conduct -- I'm in shock because normally, they're pretty straightforward to travail with."

Cisco's response

After initially refusing to discuss the matter, Cisco released a statement that said NSS Labs's proposed testing methodology "fell well short" of reflecting "the enterprise-grade nature of customer SD-WAN requirements."

"We shared real customer deployment requirements with them, but their feedback wasn't incorporated," Cisco said. "At that point, they decided not to participate in the test."

Not participating, however, usually doesn't subsist substantive the vendor will prevent the test from going forward. Typically, when a company refuses to join a test, NSS Labs will conduct it alone.

"If someone says they don't want to subsist tested, they say, 'That's great, but if a product is satisfactory enough to subsist sold to the public, it's satisfactory enough to subsist tested,'" Phatak said. "We're going to buy it, and we'll report to the public."

NSS Labs wants a refund

NSS Labs wants Cisco to refund the money spent on Viptela. It is hoping it can accept the money back without going to court.

"I hope it doesn't forward to that," Phatak said. "We haven't talked to any lawyers. I'm assuming that we'll subsist able to maintain the conversation and accept their money back."

Typically, NSS Labs buys products, and the vendors revolve them on fancy they would for any other customer.

That's a first for us, candidly. And given Cisco's ethical rules and so on -- rules of conduct -- I'm in shock. Vikram PhatakCEO, NSS Labs

NSS Labs famous Cisco's refusal to activate the Viptela purchase in its SD-WAN Comparative Report, which was the company's first SD-WAN test. Not having Cisco in the evaluation left out one of the largest SD-WAN vendors and a major tech company.

In the first quarter, London-based IHS Markit listed Cisco as No. 4 in the SD-WAN market, just behind Silver Peak. VMware was first with a 19% share, followed by Aryaka with 18%.

The NSS Labs report, released this month, compared the products of nine vendors, including VMware's NSX SD-WAN, formerly VeloCloud. VMware is Cisco's largest competitor.

NSS Labs had also planned to embrace Silver Peak in the comparison but famous it was unable to obtain the product in time for testing.

Tech companies often cite recommended ratings in NSS Labs reports in marketing materials. In April, Cisco highlighted in a blog post the organization's "recommended" rating for the Cisco Advanced Malware Protection for Endpoints product.

Based on its recent SD-WAN tests, NSS Labs recommended products from VMware, Talari Networks and Fortinet and listed products from Citrix Systems, FatPipe Networks, Forcepoint and Versa Networks as "verified." Tech buyers should reckon recommended and verified products as candidates for purchase, according to NSS Labs.

The company issued "caution" ratings for Barracuda Networks and Cradlepoint, which means companies should not deploy their products without a comprehensive evaluation, NSS Labs said.



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