index.json

[{"authors":null,"categories":null,"content":"Jinlai Xu is a PhD student of Information Science at University of Pittsburgh. His research interests include Distributed Systems, Fog/Edge and Cloud Computing, Computer Vision and Robotics. His current advisor is Balaji Palanisamy.\nBefore coming to University of Pittsburgh, he graduated from China University of Geosciences and his undergraduate and graduate advisor is Zhongwen Luo.\nFull Curriculum Vitae\n","date":-62135596800,"expirydate":-62135596800,"kind":"section","lang":"en","lastmod":-62135596800,"objectID":"69b8fb2d0e536c87502caa16461707a5","permalink":"http://jinlaixu.net/author/jinlaixu/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/jinlaixu/","section":"author","summary":"Jinlai Xu is a PhD student of Information Science at University of Pittsburgh. His research interests include Distributed Systems, Fog/Edge and Cloud Computing, Computer Vision and Robotics. His current advisor is Balaji Palanisamy.\nBefore coming to University of Pittsburgh, he graduated from China University of Geosciences and his undergraduate and graduate advisor is Zhongwen Luo.\nFull Curriculum Vitae","tags":null,"title":"","type":"author"},{"authors":null,"categories":null,"content":"Jinlai Xu is a PhD student of Information Science at University of Pittsburgh. His research interests include Distributed Systems, Fog/Edge and Cloud Computing, Computer Vision and Robotics. His current advisor is Balaji Palanisamy.\nBefore coming to University of Pittsburgh, he graduated from China University of Geosciences and his undergraduate and graduate advisor is Zhongwen Luo.\nFull Curriculum Vitae\n","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"84ad8f7284172032ce99d5a6dd94f3ca","permalink":"http://jinlaixu.net/author/jinlaixu/about/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/jinlaixu/about/","section":"author","summary":"Jinlai Xu is a PhD student of Information Science at University of Pittsburgh. His research interests include Distributed Systems, Fog/Edge and Cloud Computing, Computer Vision and Robotics. His current advisor is Balaji Palanisamy.\nBefore coming to University of Pittsburgh, he graduated from China University of Geosciences and his undergraduate and graduate advisor is Zhongwen Luo.\nFull Curriculum Vitae","tags":null,"title":"","type":"author"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"section","lang":"en","lastmod":-62135596800,"objectID":"d41d8cd98f00b204e9800998ecf8427e","permalink":"http://jinlaixu.net/author/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/","section":"author","summary":"","tags":null,"title":"Authors","type":"author"},{"authors":null,"categories":null,"content":"This feature can be used for publishing content such as:\n Project or software documentation Online courses Tutorials  The parent folder may be renamed, for example, to docs for project documentation or course for creating an online course.\nTo disable this feature, either delete the parent folder, or set draft = true in the front matter of all its pages.\nAfter renaming or deleting the parent folder, you may wish to update any [[menu.main]] menu links to it in the config.toml.\n","date":1536451200,"expirydate":-62135596800,"kind":"section","lang":"en","lastmod":1536451200,"objectID":"c3224f3a64174f08aaf31e1f1d16ffd3","permalink":"http://jinlaixu.net/tutorial/","publishdate":"2018-09-09T00:00:00Z","relpermalink":"/tutorial/","section":"tutorial","summary":"This feature can be used for publishing content such as:\n Project or software documentation Online courses Tutorials  The parent folder may be renamed, for example, to docs for project documentation or course for creating an online course.\nTo disable this feature, either delete the parent folder, or set draft = true in the front matter of all its pages.\nAfter renaming or deleting the parent folder, you may wish to update any [[menu.","tags":null,"title":"Overview","type":"docs"},{"authors":null,"categories":null,"content":" In this tutorial, I\u0026rsquo;ll share my top 10 tips for getting started with Academic:\nTip 1 \u0026hellip;\nTip 2 \u0026hellip;\n","date":1536451200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1536451200,"objectID":"6a451186c775f5f0adb3a0416d0cb711","permalink":"http://jinlaixu.net/tutorial/example/","publishdate":"2018-09-09T00:00:00Z","relpermalink":"/tutorial/example/","section":"tutorial","summary":"In this tutorial, I\u0026rsquo;ll share my top 10 tips for getting started with Academic:\nTip 1 \u0026hellip;\nTip 2 \u0026hellip;","tags":null,"title":"Example Page","type":"docs"},{"authors":null,"categories":null,"content":" Overview We develop a new contracts-based resource sharing model for federated geo-distributed clouds that allows cloud service providers to establish contractual relationships with individual datacenters for defined time intervals. Based on the established contracts, individual cloud service providers employ a cost-aware job scheduling and provisioning algorithm that enables tasks to complete and meet their response time requirements.\nWhat is the challenge?  How to decide Resource Sharing Contracts guaranteeing both Fairness and Efficiency? How to quantify utility of the contracts for the Cloud Service Providers? How to Schedule Jobs in a contracts-aware manner among the available Geo-distributed Data Centers?  How the System Works?  Auction-based Contracts Establishment (Designed based on McAfee Mechanism)  Truthfulness: No incentives to Cheat Makes the Market More Efficient and Fair Budget Balance: Keeps the Resource Allocation process Sustainable Naturally Finds the Equilibrium Between Supply and Demand  Fine-grained Utility Function Design considering:  Operating Cost Payment from Users Penalty for Violating SLAs  Resource Sharing Contracts  Relatively Long Contracts Avoid Frequent Migrations Predetermined Allocations minimize Disruption of Normal Operations in the Data Centers  Cost-aware Scheduling  Optimize the Cost of Using Contracts   Publications\n Jinlai Xu, Balaji Palanisamy (2017). Cost-aware Resource Management for Federated Clouds Using Resource Sharing Contracts, IEEE Cloud 2017. Jinlai Xu, Balaji Palanisamy (2018). Optimized Contract-based Model for Resource Allocation in Federated Geo-distributed Clouds, IEEE TSC.  Slides\n ","date":1517270400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1517270400,"objectID":"da781b399c91029bbf74c7f6caf51347","permalink":"http://jinlaixu.net/project/contracts/","publishdate":"2018-01-30T00:00:00Z","relpermalink":"/project/contracts/","section":"project","summary":"A Contracts-based Federated Cloud Architecture","tags":["cloud","game-theory"],"title":"Contracts Federated Cloud","type":"project"},{"authors":["Jinlai Xu","Balaji Palanisamy"],"categories":null,"content":"","date":1516060800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1516060800,"objectID":"35d8775ed77b30163885a03ce5707a94","permalink":"http://jinlaixu.net/publication/contract-tsc-2018/","publishdate":"2018-01-16T00:00:00Z","relpermalink":"/publication/contract-tsc-2018/","section":"publication","summary":"In the era of Big Data, with data growing massively in scale and velocity, cloud computing and its pay-as-you-go modelcontinues to provide significant cost benefits and a seamless service delivery model for cloud consumers. The evolution of small-scaleand large-scale geo-distributed datacenters operated and managed by individual Cloud Service Providers (CSPs) raises newchallenges in terms of effective global resource sharing and management of autonomously-controlled individual datacenter resourcestowards a globally efficient resource allocation model. Earlier solutions for geo-distributed clouds have focused primarily on achievingglobal efficiency in resource sharing, that although tries to maximize the global resource allocation, results in significant inefficiencies inlocal resource allocation for individual datacenters and individual cloud provi ders leading to unfairness in their revenue and profitearned. In this paper, we propose a new contracts-based resource sharing model for federated geo-distributed clouds that allows CSPsto establish resource sharing contracts with individual datacentersapriorifor defined time intervals during a 24 hour time period. Basedon the established contracts, individual CSPs employ a contracts cost and duration aware job scheduling and provisioning algorithmthat enables jobs to complete and meet their response time requirements while achieving both global resource allocation efficiency andlocal fairness in the profit earned. The proposed techniques are evaluated through extensive experiments using realistic workloadsgenerated using the SHARCNET cluster trace. The experiments demonstrate the effectiveness, scalability and resource sharingfairness of the proposed model.","tags":null,"title":"Optimized Contract-based Model for Resource Allocation in Federated Geo-distributed Clouds","type":"publication"},{"authors":null,"categories":null,"content":" Overview PADS is a strategy-proof differentially private auction mechanism designed to allow cloud providers to trade resources with consumers in such a way that individual bidding information of the consumers do not get exposed through the auction mechanism. PADS provides differential privacy and approximate truthfulness guarantees while maintaining good performance in terms of revenue earned and allocation efficiency.\nWhat is the challenge?  Protect Each User\u0026rsquo;s Bidding Information through the Auctioning Mechanism. Randomize to achieve Differential Privacy while also ensuring Higher Resource Allocation Utility.  How the System Works?  PADS-ADP Scheme:  Iterative Exponential Mechanism: in every iteration, the mechanism chooses one winner from the bidders using an Exponential Mechanism until all bids are selected or all the resources get allocated. Approximate Differential Privacy: PADS-ADP can provide $(\\epsilon,\\delta)$-differential privacy. Truthfulness: PADS-ADP is truthful independent of the strategies used by the bidders.  PADS-DP:  Grouping Exponential Mechanism: the bids are grouped by the possible price outcomes. Differential Privacy: PADS-DP can provide $\\epsilon$-differential privacy. Approximate Truthfulness: PADS-DP is $\\epsilon\\Delta$-truthful and it is independentof the strategies used by the bidders.   Publications\n Jinlai Xu, Balaji Palanisamy, Yuzhe Tang, S.D. Madhu Kumar (2017). PADS: Privacy-preserving Auction Design for Allocating Dynamically Priced Cloud Resources, IEEE CIC 2017.  ","date":1508025600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1508025600,"objectID":"ecba867a92d61604dfe14f02a1b55381","permalink":"http://jinlaixu.net/project/pads/","publishdate":"2017-10-15T00:00:00Z","relpermalink":"/project/pads/","section":"project","summary":"Privacy-preserving Dynamic Price on Cloud Resource Allocation","tags":["cloud","privacy"],"title":"PADS","type":"project"},{"authors":["Jinlai Xu","Balaji Palanisamy","Yuzhe Tang","S.D. Madhu Kumar"],"categories":null,"content":"","date":1508025600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1508025600,"objectID":"637900469f304c0714f8a724c5983b70","permalink":"http://jinlaixu.net/publication/pads-2017/","publishdate":"2017-10-15T00:00:00Z","relpermalink":"/publication/pads-2017/","section":"publication","summary":"With the rapid growth of Cloud Computing technologies, enterprises are increasingly deploying their services in the Cloud. Dynamically priced cloud resources such as the Amazon EC2 Spot Instance provides an efficient mechanism for cloud service providers to trade resources with potential buyers using an auction mechanism. With the dynamically priced cloud resource markets, cloud consumers can buy resources at a significantly lower cost than statically priced cloud resources such as the on-demand instances in Amazon EC2. While dynamically priced cloud resources enable to maximize datacenter resource utilization and minimize cost for the consumers, unfortunately, such auction mechanisms achieve these benefits only at a cost significant of private information leakage. In an auction-based mechanism, the private information includes information on the demands of the consumers that can lead an attacker to understand the current computing requirements of the consumers and perhaps even allow the inference of the workload patterns of the consumers. In this paper, we propose PADS, a strategy-proof differentially private auction mechanism that allows cloud providers to privately trade resources with cloud consumers in such a way that individual bidding information of the cloud consumers is not exposed by the auction mechanism. We demonstrate that PADS achieves differential privacy and approximate truthfulness guarantees while maintaining good performance in terms of revenue gains and allocation efficiency. We evaluate PADS through extensive simulation experiments that demonstrate that in comparison to traditional auction mechanisms, PADS achieves relatively high revenues for cloud providers while guaranteeing the privacy of the participating consumers.","tags":null,"title":"PADS: Privacy-preserving Auction Design forAllocating Dynamically Priced Cloud Resources","type":"publication"},{"authors":["Jinlai Xu","Balaji Palanisamy"],"categories":null,"content":"","date":1498348800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1498348800,"objectID":"a7345b0d6380b2b01c7303e776c15932","permalink":"http://jinlaixu.net/publication/contract-cloud-2017/","publishdate":"2017-06-25T00:00:00Z","relpermalink":"/publication/contract-cloud-2017/","section":"publication","summary":"Cloud computing and its pay-as-you-go model continue to provide significant cost benefits and a seamless service delivery model for cloud consumers. The evolution of small-scale and large-scale geo-distributed datacenters operated and managed by individual cloud service providers raises new challenges in terms of effective global resource sharing and management of autonomously-controlled individual datacenter resources. Earlier solutions for geo-distributed clouds have focused primarily on achieving global efficiency in resource sharing that results in significant inefficiencies in local resource allocation for individual datacenters leading to unfairness in revenue and profit earned. In this paper, we propose a new contracts-based resource sharing model for federated geo-distributed clouds that allows cloud service providers to establish resource sharing contracts with individual datacenters apriori for defined time intervals during a 24 hour time period. Based on the established contracts, individual cloud service providers employ a cost-aware job scheduling and provisioning algorithm that enables tasks to complete and meet their response time requirements. The proposed techniques are evaluated through extensive experiments using realistic workloads and the results demonstrate the effectiveness, scalability and resource sharing efficiency of the proposed model. ","tags":null,"title":"Cost-aware Resource Management for Federated Clouds Using Resource Sharing Contracts","type":"publication"},{"authors":["Jinlai Xu","Balaji Palanisamy","Heiko Ludwig","Qingyang Wang"],"categories":null,"content":"","date":1498348800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1498348800,"objectID":"cf0785e51b097b9b793b64eb4025fa79","permalink":"http://jinlaixu.net/publication/zenith-edge-2017/","publishdate":"2017-06-25T00:00:00Z","relpermalink":"/publication/zenith-edge-2017/","section":"publication","summary":"In the Internet of Things(IoT) era, the demands for low-latency computing for time-sensitive applications (e.g., location-based augmented reality games, real-time smart grid management, real-time navigation using wearables) has been growing rapidly. Edge Computing provides an additional layer of infrastructure to fill latency gaps between the IoT devices and the back-end computing infrastructure. In the edge computing model, small-scale micro-datacenters that represent ad-hoc and distributed collection of computing infrastructure pose new challenges in terms of management and effective resource sharing to achieve a globally efficient resource allocation. In this paper, we propose Zenith, a novel model for allocating computing resources in an edge computing platform that allows service providers to establish resource sharing contracts with edge infrastructure providers apriori. Based on the established contracts, service providers employ a latency-aware scheduling and resource provisioning algorithm that enables tasks to complete and meet their latency requirements. The proposed techniques are evaluated through extensive experiments that demonstrate the effectiveness, scalability and performance efficiency of the proposed model.","tags":null,"title":"Zenith: Utility-aware Resource Allocation for Edge Computing","type":"publication"},{"authors":null,"categories":null,"content":"Intro I have moved my homepage to Hugo which I think is the fastest static website generator. On the other hand, I like the theme - Acadmic which is best for the persons who like me is in academia.\nHowever, there is one problem that Hugo can not be automatically generated as Jekyll in Github Page. I referred to the solution which is provided by the author of Acadmic 1 to use Wercker to automatically generate the pages from source files to HTML. The processes are as below.\nSetup Website You need to firstly setup a hugo website, test it locally and upload to one of your github repositories. I assume the repository is homepage.\nCreate Personal Website Repository I use xujinlai.github.io as the personal website repository.\nSetup Wercker You can treat Wercker as an automatically builder which can build your website imediatly after you push your update with git.\nIt is also very easy to use, which you just need to setup an application on Wercker with logining wity your Github account. You just need to follow the instruction below to setup the automatically builder for your website:\n Login to Wercker with your github account such as Jinlai Xu. Add an application and setup the repository of the application to homepage. Add an wercker.yml file in your repository for example Wercker.yml example You need to modify the above wercker.yml with your own repo name and your own domain name. Add a $GIT_TOKEN parameter in your wercker application to make it possible to access your github repositories. You can refer to 2 to get the details. Add a deploy step after build step in your wercker application\u0026rsquo;s Workflows page. Upload you website to homepage to test if your wercker application is automatically run.  Wercker.yml example box: golang build: steps: - add-to-known_hosts: hostname: github.com fingerprint: 16:27:ac:a5:76:28:2d:36:63:1b:56:4d:eb:df:a6:48 type: rsa - script: name: initialize and update git submodules code: | git submodule init git submodule update --remote --recursive - arjen/hugo-build: version: \u0026quot;0.20.7\u0026quot; deploy: steps: - lukevivier/gh-pages: token: $GIT_TOKEN basedir: public repo: xujinlai/xujinlai.github.io domain: jinlaixu.net   Create a Free Personal Academic Website with Hugo ^ Creating a personal access token for the command line ^  ","date":1496188800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1508025600,"objectID":"7b5b9513d4de000774d3fc8c31ec2632","permalink":"http://jinlaixu.net/post/move-to-hugo/","publishdate":"2017-05-31T00:00:00Z","relpermalink":"/post/move-to-hugo/","section":"post","summary":"Intro I have moved my homepage to Hugo which I think is the fastest static website generator. On the other hand, I like the theme - Acadmic which is best for the persons who like me is in academia.\nHowever, there is one problem that Hugo can not be automatically generated as Jekyll in Github Page. I referred to the solution which is provided by the author of Acadmic 1 to use Wercker to automatically generate the pages from source files to HTML. The processes are as below.\n","tags":[],"title":"Move to Hugo","type":"post"},{"authors":null,"categories":null,"content":" Overview In the Internet of Things(IoT) era, the demands for low-latency computing for time-sensitive applications (e.g., location-based augmented reality games, real-time smart grid management, real-time navigation using wearables) has been growing rapidly. Edge Computing provides an additional layer of infrastructure to fill latency gaps between IoT devices and the backend computing infrastructure.\nZenith decouples resource allocation and service provisioning and management at the edge and provides a novel model to allocate resources at the edge to maximize utility. Zenith aims at increasing the overall resource allocation efficiency by allowing resources to be allocated and shared in a latency-aware manner.\nWhat is the challenge?  Fair and Efficient Resource Allocation across Geographically Distributed Edge Resources Fast Service Discovery on Highly Distributed Micro Data Centers  How Zenith Works?  Geographic Division usingWeighted Vonoroi Diagram (WVD)  Service Discovery with Constant Time Naturally Satisfies Latency-Sensitive Workloads with Nearest Service Discovery Dynamically Balances the Workloads Between Micro Data Centers Easy and Fast to Update  Auction-based Resource Allocation  Truthfulness: No Incentives for Cheating. Budget Balance: Keeps the Allocation Process more Sustainable Naturally Finds the Equilibrium Between Supply and Demand  Resource Sharing Contracts  Longer duration of Contracts Avoid Frequent Migrations Stable Running Environment Suits the Requirements for Latency-Sensitive Workloads   Publications\n Jinlai Xu, Balaji Palanisamy, Heiko Ludwig, Qingyang Wang (2017). Zenith: Utility-aware Resource Allocation for Edge Computing, IEEE Edge 2017.  Slides\n ","date":1495584000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1495584000,"objectID":"e5bb11ec9339926fdbdbe3e8db838486","permalink":"http://jinlaixu.net/project/zenith/","publishdate":"2017-05-24T00:00:00Z","relpermalink":"/project/zenith/","section":"project","summary":"An Innovation on Edge Computing Architecture","tags":["edge","game-theory"],"title":"Zenith","type":"project"},{"authors":["Hong Yao","Jinlai Xu","Zhongwen Luo","Deze Zeng"],"categories":null,"content":"","date":1449446400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1449446400,"objectID":"a583ec4d04ff2e451251fcdb73c4e988","permalink":"http://jinlaixu.net/publication/memomr-ccpe/","publishdate":"2015-12-07T00:00:00Z","relpermalink":"/publication/memomr-ccpe/","section":"publication","summary":"MapReduce has been widely regarded as a flexible, scalable, and easy-to-use distributed programming paradigm for big data processing such as social network data analysis on cloud computing platforms. To embrace the upcoming of big data era, many efforts have been devoted to accelerating the MapReduce performance from different aspects, especially intermediate result reusing like Dache. In this paper, we observe that existing intermediate result reusing mechanism is not efficient enough as many I/O operations are wasted. Efficient reusing of the intermediate results could potentially improve the MapReduce performance. Inspired by such fact, we propose a framework named MEMoMR (more efficient intermediate result reusing for MapReduce) by introducing a novel reusing mechanism that can substantially reduce the I/O overhead. To this end, we invent a new metadata description method and apply it in the reusing phase. We practically realize MEMoMR and evaluate its performance by implementing it in a real cluster. The experiment results show that MEMoMR can improve the system performance as high as 23.4%, comparing against Dache.","tags":null,"title":"MEMoMR: Accelerate MapReduce via reuse of intermediate results","type":"publication"},{"authors":null,"categories":null,"content":" Welcome to Slides Academic\nFeatures  Efficiently write slides in Markdown 3-in-1: Create, Present, and Publish your slides Supports speaker notes Mobile friendly slides  Controls  Next: Right Arrow or Space Previous: Left Arrow Start: Home Finish: End Overview: Esc Speaker notes: S Fullscreen: F Zoom: Alt + Click PDF Export: E  Code Highlighting Inline code: variable\nCode block:\nporridge = \u0026quot;blueberry\u0026quot; if porridge == \u0026quot;blueberry\u0026quot;: print(\u0026quot;Eating...\u0026quot;)  Math In-line math: $x + y = z$\nBlock math:\n$$ f\\left( x \\right) = \\;\\frac{{2\\left( {x + 4} \\right)\\left( {x - 4} \\right)}}{{\\left( {x + 4} \\right)\\left( {x + 1} \\right)}} $$\nFragments Make content appear incrementally\n{{% fragment %}} One {{% /fragment %}} {{% fragment %}} **Two** {{% /fragment %}} {{% fragment %}} Three {{% /fragment %}}  Press Space to play!\nOne  Two  Three \nA fragment can accept two optional parameters:\n class: use a custom style (requires definition in custom CSS) weight: sets the order in which a fragment appears  Speaker Notes Add speaker notes to your presentation\n{{% speaker_note %}} - Only the speaker can read these notes - Press `S` key to view {{% /speaker_note %}}  Press the S key to view the speaker notes!\n Only the speaker can read these notes Press S key to view   Themes  black: Black background, white text, blue links (default) white: White background, black text, blue links league: Gray background, white text, blue links beige: Beige background, dark text, brown links sky: Blue background, thin dark text, blue links   night: Black background, thick white text, orange links serif: Cappuccino background, gray text, brown links simple: White background, black text, blue links solarized: Cream-colored background, dark green text, blue links  Custom Slide Customize the slide style and background\n{{\u0026lt; slide background-image=\u0026quot;/img/boards.jpg\u0026quot; \u0026gt;}} {{\u0026lt; slide background-color=\u0026quot;#0000FF\u0026quot; \u0026gt;}} {{\u0026lt; slide class=\u0026quot;my-style\u0026quot; \u0026gt;}}  Custom CSS Example Let\u0026rsquo;s make headers navy colored.\nCreate assets/css/reveal_custom.css with:\n.reveal section h1, .reveal section h2, .reveal section h3 { color: navy; }  Questions? Ask\nDocumentation\n","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"c2915ec5da95791851caafdcba9664af","permalink":"http://jinlaixu.net/slides/example-slides/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/slides/example-slides/","section":"slides","summary":"Welcome to Slides Academic\nFeatures  Efficiently write slides in Markdown 3-in-1: Create, Present, and Publish your slides Supports speaker notes Mobile friendly slides  Controls  Next: Right Arrow or Space Previous: Left Arrow Start: Home Finish: End Overview: Esc Speaker notes: S Fullscreen: F Zoom: Alt + Click PDF Export: E  Code Highlighting Inline code: variable\nCode block:\nporridge = \u0026quot;blueberry\u0026quot; if porridge == \u0026quot;blueberry\u0026quot;: print(\u0026quot;Eating...\u0026quot;)  Math In-line math: $x + y = z$","tags":null,"title":"Slides","type":"slides"}]