The document discusses named entity recognition and part-of-speech tagging. It provides examples of using Bi-LSTM neural networks with CRF layers for sequence labeling tasks and links to resources for pre-trained models and labeled datasets. Key algorithms and tasks mentioned include Bi-LSTM, CRF, named entity extraction, and part-of-speech tagging.
DataPlotly is a plugin for QGIS that allows to create D3 like plots from spatial data. It is build on top of plotly, a javascript library which offers easy API for many languages such as Python, R, Matlab and NodeJS.
The plugin was created back in 2017 for the upcoming QGIS 3 version: today the plugin has been downloaded more than 50,000 times.
Creating plots is out of the main scopes of QGIS but thanks to the simple Python API it is easy enough to create additional scripts and plugins. Thanks to these APIs, DataPlotly is today a well maintained Python plugin with a growing community of developers, users and testers.
DataPlotly plots are completely interactive so that plot elements are directly linked with map items; therefore the user is able to query map items from the main plot canvas.
Thanks to a crowdfunding campaign launched in March 2019 during the annual QGIS User Conference, the functionalities of DataPlotly were extended: a complete refactoring of the code, more plots but especially the creation of plots in the layout composer.
More and more people are using the plugin to analyze the data and to create complex output reports of data (e.g. the Covid-19 pandemic
This document provides information about Argo Projects, including:
1. Argo Projects allow grouping applications and defining project-level settings like source repositories and destination clusters.
2. An example Argo Project config is shown that sets a description, allows all source repositories, and defines a single destination namespace and cluster.
3. Project-level resource whitelisting and blacklisting is demonstrated for namespaces and resource quotas.
Chaos Engineering on Microservices - 윤석찬, AWS 테크에반젤리스트 Channy Yun
This document contains information about chaos engineering and experimenting with latency injection between microservices. It discusses distributing traffic between production, control, and experimental versions of a service called Service A. 98% of traffic would go to the production version, 1% to the control version, and 1% to the experimental version where latency is injected between Service A and downstream services using an injector. This allows experimenting with how systems react to different latency conditions to test resilience and identify problems.
This document contains information about deploying a Kintone application pod (AP) on Kubernetes, including the deployment configuration, services, and environment variables. Key details are the use of the Quay.io Kintone image, setting the FTS Elasticsearch host as an environment variable, and defining a headless service for the AP.
2018년 2월 24일 KCD2018에서 Google Polymer에 대하여 발표한 내용입니다. 이 발표에서는 웹, 하이브리드 앱 및 프로그레시브 웹 앱 개발을 위한 구글의 웹컴포넌트 라이브러리인 폴리머를 쉽고 재미있게 다룹니다. 웹컴포넌트, 폴리머에 대한 소개와 함께 폴리머 2.0의 특징을 소개합니다. 또한 modulizer, TypeScript, yarn, webpack의 도입을 추진하고 있는 폴리머 3.0 알파 버전의 주요변화를 알아봅니다.
- This document contains configuration files for deploying an application called "jkd" to a Kubernetes cluster using Deployments and Services.
- It also discusses using GitOps for infrastructure as code where application code and Kubernetes manifests are maintained in a git repository and applied to clusters automatically through pull requests and merges.
- The document recommends Weaveworks for their GitOps approach of using Operators to apply Kubernetes resources from git in an automated and consistent manner.
DataPlotly is a plugin for QGIS that allows to create D3 like plots from spatial data. It is build on top of plotly, a javascript library which offers easy API for many languages such as Python, R, Matlab and NodeJS.
The plugin was created back in 2017 for the upcoming QGIS 3 version: today the plugin has been downloaded more than 50,000 times.
Creating plots is out of the main scopes of QGIS but thanks to the simple Python API it is easy enough to create additional scripts and plugins. Thanks to these APIs, DataPlotly is today a well maintained Python plugin with a growing community of developers, users and testers.
DataPlotly plots are completely interactive so that plot elements are directly linked with map items; therefore the user is able to query map items from the main plot canvas.
Thanks to a crowdfunding campaign launched in March 2019 during the annual QGIS User Conference, the functionalities of DataPlotly were extended: a complete refactoring of the code, more plots but especially the creation of plots in the layout composer.
More and more people are using the plugin to analyze the data and to create complex output reports of data (e.g. the Covid-19 pandemic
This document provides information about Argo Projects, including:
1. Argo Projects allow grouping applications and defining project-level settings like source repositories and destination clusters.
2. An example Argo Project config is shown that sets a description, allows all source repositories, and defines a single destination namespace and cluster.
3. Project-level resource whitelisting and blacklisting is demonstrated for namespaces and resource quotas.
Chaos Engineering on Microservices - 윤석찬, AWS 테크에반젤리스트 Channy Yun
This document contains information about chaos engineering and experimenting with latency injection between microservices. It discusses distributing traffic between production, control, and experimental versions of a service called Service A. 98% of traffic would go to the production version, 1% to the control version, and 1% to the experimental version where latency is injected between Service A and downstream services using an injector. This allows experimenting with how systems react to different latency conditions to test resilience and identify problems.
This document contains information about deploying a Kintone application pod (AP) on Kubernetes, including the deployment configuration, services, and environment variables. Key details are the use of the Quay.io Kintone image, setting the FTS Elasticsearch host as an environment variable, and defining a headless service for the AP.
2018년 2월 24일 KCD2018에서 Google Polymer에 대하여 발표한 내용입니다. 이 발표에서는 웹, 하이브리드 앱 및 프로그레시브 웹 앱 개발을 위한 구글의 웹컴포넌트 라이브러리인 폴리머를 쉽고 재미있게 다룹니다. 웹컴포넌트, 폴리머에 대한 소개와 함께 폴리머 2.0의 특징을 소개합니다. 또한 modulizer, TypeScript, yarn, webpack의 도입을 추진하고 있는 폴리머 3.0 알파 버전의 주요변화를 알아봅니다.
- This document contains configuration files for deploying an application called "jkd" to a Kubernetes cluster using Deployments and Services.
- It also discusses using GitOps for infrastructure as code where application code and Kubernetes manifests are maintained in a git repository and applied to clusters automatically through pull requests and merges.
- The document recommends Weaveworks for their GitOps approach of using Operators to apply Kubernetes resources from git in an automated and consistent manner.
신뢰성 높은 클라우드 기반 서비스 운영을 위한 Chaos Engineering in Action (윤석찬, AWS 테크에반젤리스트) :: ...Amazon Web Services Korea
This document discusses concepts related to chaos engineering including:
1. Many large tech companies like Amazon, Netflix, and Google practice chaos engineering to test system resiliency through failure injection and destruction testing.
2. Netflix developed open source tools like Chaos Monkey and Simian Army to randomly terminate instances and components to ensure applications can withstand infrastructure failures.
3. Chaos engineering involves injecting failures in a controlled way to test recovery capabilities and uncover weaknesses before they impact real users during production outages.
1. The document discusses a method for tracking objects in videos by colorizing videos in a self-supervised manner without manual annotations.
2. Key steps include colorizing each frame based on a reference frame, extracting features from the colorized frames to match objects across frames, and using the matches to propagate colors and improve tracking over time.
3. The method is able to track objects as they move, rotate or change scale in videos without any object segmentations or pose annotations during training or testing.
kintone on EKS ― EKS で実現するインフラ自動構築パイプライン Yusuke Nojima
This document discusses a Kubernetes deployment configuration for a Kintone application platform (AP). It specifies using the Quay.io kintone image tagged with {{ tag "kintone" }}, setting environment variables like FTS_ELASTICSEARCH_HOST from CloudFormation exports, and using an image pull secret. A headless service is also defined for the AP with an external DNS annotation.
This document discusses edge computing and cloud computing beyond traditional data centers. It describes how edge computing distributes computing, storage and applications away from centralized points to the logical extremes of a network. This allows for more distributed and localized processing of data, with the goal of improving response times and bandwidth usage for applications and use cases that require low latency and real-time responsiveness. Edge computing helps enable applications in areas like industrial automation, smart cities and autonomous vehicles that need rapid access to data with minimal delays.
This document contains an agenda and notes from a technical discussion. It includes topics like Kubernetes, etcd, the operator framework, Kafka installation on OpenShift, Zookeeper, and configuration management. Various technical concepts and components are defined briefly.
This document discusses DevOps tools and practices on Kubernetes and OpenShift container platforms. It covers topics like:
1. Using Jenkins as a service on OpenShift for continuous integration and delivery.
2. Deploying web applications and microservices on Kubernetes, including technologies like circuit breakers.
3. Architectures for distributed and microservices systems, including service meshes.
4. DevOps tools available on OpenShift like Istio for traffic management between microservices.
Poetic Dream - Deep Learning Maker Portfolio (Harvard REA Version)Kevin (Yu-Teng) Li
Poetic Dream is a poetry visualization project that uses deep learning techniques like natural language processing, image captioning APIs, and neural style transfer to analyze a poem and generate a corresponding artwork. It involves preprocessing the text with NLP, searching image databases for relevant content, applying the content image to a style image using neural style transfer, and designing a graphical user interface to display the generated artworks along with the original poem. The project aims to explore how text can inspire visual art but faces limitations from computational costs and data availability.
This document discusses several architectures for building multiplayer game backends on AWS. It describes using EC2 instances, load balancers, DynamoDB, ElastiCache, and CloudFront to host game servers and handle data, and services like Lambda, Kinesis, and GameLift for real-time features. Automatic scaling is enabled through Auto Scaling groups to handle fluctuating player loads.
[DL輪読会]A Style-Based Generator Architecture for Generative Adversarial NetworksDeep Learning JP
This document discusses style-based generative adversarial networks and techniques used in them. It introduces adaptive instance normalization (AdaIN) which aligns the mean and variance of features to match a target style. It also discusses mixing regularization which combines styles at the latent space level and perceptual path length which measures diversity of generated images.
This document contains notes from game design presentations and resources. It discusses topics like building conflict and challenge in games, different types of conflicts that can arise, techniques for differentiating enemies, using hit points and mana, and tips for platformer jumping mechanics. Links are provided to game design resources from the Game Developers Conference (GDC), websites, and YouTube videos discussing topics like magic tricks in game design, making enemies distinct, and GameMaker platformer development.
신뢰성 높은 클라우드 기반 서비스 운영을 위한 Chaos Engineering in Action (윤석찬, AWS 테크에반젤리스트) :: ...Amazon Web Services Korea
This document discusses concepts related to chaos engineering including:
1. Many large tech companies like Amazon, Netflix, and Google practice chaos engineering to test system resiliency through failure injection and destruction testing.
2. Netflix developed open source tools like Chaos Monkey and Simian Army to randomly terminate instances and components to ensure applications can withstand infrastructure failures.
3. Chaos engineering involves injecting failures in a controlled way to test recovery capabilities and uncover weaknesses before they impact real users during production outages.
1. The document discusses a method for tracking objects in videos by colorizing videos in a self-supervised manner without manual annotations.
2. Key steps include colorizing each frame based on a reference frame, extracting features from the colorized frames to match objects across frames, and using the matches to propagate colors and improve tracking over time.
3. The method is able to track objects as they move, rotate or change scale in videos without any object segmentations or pose annotations during training or testing.
kintone on EKS ― EKS で実現するインフラ自動構築パイプライン Yusuke Nojima
This document discusses a Kubernetes deployment configuration for a Kintone application platform (AP). It specifies using the Quay.io kintone image tagged with {{ tag "kintone" }}, setting environment variables like FTS_ELASTICSEARCH_HOST from CloudFormation exports, and using an image pull secret. A headless service is also defined for the AP with an external DNS annotation.
This document discusses edge computing and cloud computing beyond traditional data centers. It describes how edge computing distributes computing, storage and applications away from centralized points to the logical extremes of a network. This allows for more distributed and localized processing of data, with the goal of improving response times and bandwidth usage for applications and use cases that require low latency and real-time responsiveness. Edge computing helps enable applications in areas like industrial automation, smart cities and autonomous vehicles that need rapid access to data with minimal delays.
This document contains an agenda and notes from a technical discussion. It includes topics like Kubernetes, etcd, the operator framework, Kafka installation on OpenShift, Zookeeper, and configuration management. Various technical concepts and components are defined briefly.
This document discusses DevOps tools and practices on Kubernetes and OpenShift container platforms. It covers topics like:
1. Using Jenkins as a service on OpenShift for continuous integration and delivery.
2. Deploying web applications and microservices on Kubernetes, including technologies like circuit breakers.
3. Architectures for distributed and microservices systems, including service meshes.
4. DevOps tools available on OpenShift like Istio for traffic management between microservices.
Poetic Dream - Deep Learning Maker Portfolio (Harvard REA Version)Kevin (Yu-Teng) Li
Poetic Dream is a poetry visualization project that uses deep learning techniques like natural language processing, image captioning APIs, and neural style transfer to analyze a poem and generate a corresponding artwork. It involves preprocessing the text with NLP, searching image databases for relevant content, applying the content image to a style image using neural style transfer, and designing a graphical user interface to display the generated artworks along with the original poem. The project aims to explore how text can inspire visual art but faces limitations from computational costs and data availability.
This document discusses several architectures for building multiplayer game backends on AWS. It describes using EC2 instances, load balancers, DynamoDB, ElastiCache, and CloudFront to host game servers and handle data, and services like Lambda, Kinesis, and GameLift for real-time features. Automatic scaling is enabled through Auto Scaling groups to handle fluctuating player loads.
[DL輪読会]A Style-Based Generator Architecture for Generative Adversarial NetworksDeep Learning JP
This document discusses style-based generative adversarial networks and techniques used in them. It introduces adaptive instance normalization (AdaIN) which aligns the mean and variance of features to match a target style. It also discusses mixing regularization which combines styles at the latent space level and perceptual path length which measures diversity of generated images.
This document contains notes from game design presentations and resources. It discusses topics like building conflict and challenge in games, different types of conflicts that can arise, techniques for differentiating enemies, using hit points and mana, and tips for platformer jumping mechanics. Links are provided to game design resources from the Game Developers Conference (GDC), websites, and YouTube videos discussing topics like magic tricks in game design, making enemies distinct, and GameMaker platformer development.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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9. X = (x1, x2, x3, x4),
Y = (y1, y2, y3, y4),<latexit sha1_base64="3DGdpXNuQh2Tm2FSOQvwYFyI1M8=">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</latexit><latexit sha1_base64="3DGdpXNuQh2Tm2FSOQvwYFyI1M8=">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</latexit><latexit sha1_base64="3DGdpXNuQh2Tm2FSOQvwYFyI1M8=">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</latexit><latexit sha1_base64="3DGdpXNuQh2Tm2FSOQvwYFyI1M8=">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</latexit>
:
:
h = Bi-LSTM(X),
Y = CRF(h),
where h = (h1, h2, h3, h4).<latexit sha1_base64="xp/pcaJ+VIRARYJ14oKeTdMeDig=">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</latexit><latexit sha1_base64="xp/pcaJ+VIRARYJ14oKeTdMeDig=">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</latexit><latexit sha1_base64="xp/pcaJ+VIRARYJ14oKeTdMeDig=">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</latexit><latexit sha1_base64="xp/pcaJ+VIRARYJ14oKeTdMeDig=">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</latexit>
9
38. 5APM G M DE A PMAN B M 5 A I E T 7A CIE E I
3 GA A G 5 -3
I AI 8ALPAI A 3 AGEIC E E EMA E I G 38 4 -55N -70
4 A G -3
8A E NP AM ENA NALPAI A CCEIC RE D E EMA E I G G ICP CA AGN
6A AMN A G -3
.AA I AS P GEUA R M MA MANAI E IN
6A AMN A G 5 -3
RAM 8ALPAI A 3 AGEIC RE D NF R MA 5APM G 3 ICP CA 4 AG
3EP A G 2
5 GG I AS N MA MA A ALP G A AM R M MA MANAI E IN RE D ME GA AI E I
3EIC A G 4536
:I AMN I EIC DA EBBE PG T B M EIEIC AA BAA B MR M IAPM G IA R MFN
1G M A G 28 8
BBE EAI F M 3A-PI A G 5APM G 5A R MF ME FN B DA M A
38