Update 19 Sept 2011 - The Lib Dem Motion "Protecting Individuals and Communities from Drug Harms" was passed by conference, and is therefore now Lib Dem Party policy. Watch the full conference debate you tube here.
The Lib Dems have long held the most reasonable, evidence based drug policy of the three biggest UK parties, and if passed, their latest drugs motion would build on that history in grand style. At its heart is a call for a rather dull sounding "Impact Assessment" of the Misuse of Drugs Act 1971 (MDA) by an independent panel, to compare all the costs and benefits of the current approach to drugs with alternatives, such as Portuguese style decriminalisation of possession, and models of legal regulation including for cannabis, and Swiss-style heroin prescription. This is an eminently sensible approach that would allow future drug policy to be based on evidence of what works, rather than political rhetoric or tabloid fever.
But make no mistake – it would be truly groundbreaking. Firstly, none of the many previous reviews of drug policy have done this kind of comprehensive comparison. (In the US, the Drug Czar's Office is actually banned from doing research that might show benefits from legal regulation.) Secondly, it would also be far harder to ignore because it would have been Government initiated, originate from Lib Dem Party Policy, and be of huge public interest, like the recent Banking Review.
Yet an Impact Assessment of this kind is not radical - it is the gold-standard approach for assessing public policy the world over. In fact, if the Misuse of Drugs Act were introduced today it would be required to have one carried out on it in advance of enactment, and after 3-5 years to see if it is actually doing what it was intended to.
This has been a requirement for all Acts of Parliament since 2005 for the very good reason that laws often have unintended consequences, and it makes sense to assess whether they outweigh the benefits. And if there is one thing everyone agrees about in drug policy it is that the MDA (and similar legislation in other countries) was not intended to create a huge criminal market; undermine international development and security; increase health harms including HIV/AIDS; promote stigma and discrimination; lead to deforestation and pollution, and undermine human rights all over the globe (see countthecosts.org for details). Yet even the UN Office on Drugs and Crime, which oversees the system of global prohibition, acknowledges it has created all these 'unintended consequences'.
Would such an Impact Assessment be possible? Despite what the current and last Government have said, yes it would be – which is why the European Commission is starting one looking at different approaches to controlling new synthetic drugs - so-called 'legal highs'. What if there are gaps in the research base? Then one of the key things an IA can do is identify them and provide guidance in prioritising which gaps should be filled first, and which are not crucial to forming an overall conclusion. That is why an Impact Assessment approach is endorsed by a range of NGOs and academics from various sectors affected by drug policy, and politicians from all parties, including the Conservative Peer and Professor of Government Lord Norton, who was the prime mover in making Impact Assessments obligatory for all new Acts. Impact Assessments may not make for great sound bites or lurid tabloid headlines, but they do result in better policy, and at a time of severe cuts, there is a pressing need to ensure all public spending is as effective as possible.
This motion is also timely. Globally, the tide has turned, and ever more prominent statesmen and women are calling for an end to the failed war on drugs and its replacement with models of legal regulation based on science, public health and human rights principles. Those voices now include the former Secretary-General of the UN Kofi Annan, seven former Presidents including of the US, Mexico, Brazil, Colombia, Honduras and Switzerland, and the current Prime Minister of Greece, not to mention senior doctors, police, academics and religious leaders. In response, the Tory and Labour frontbenches continue shouting; "Drugs are bad for you, ban them" whilst refusing to explore different approaches – even when shown to work in other countries. That is not principled leadership, and looks increasingly irresponsible.
The Lib Dem motion is only part of a much broader, growing global campaign for a full review of the War on Drugs, and all the alternatives. The War on Drugs: Count the Costs project (www.countthecosts.org) was launched this year to mark the 50th Anniversary of the UN Convention that underpins the current approach. It is bringing together groups and individuals from every sector affected by drugs, and every region. We might have a range of views on what we should do about drugs, but we share a commitment to make the world a safer, healthier place. As a result we also share one simple aim – let's count the costs of the war on drugs and explore the alternatives, so we can base drug policy on evidence of what works. And an Impact Assessment of the UK Misuse of Drugs Act would be an excellent place to start.
With the ever increasing importance of Latin America to both the debate around drug policy, and the politics of ending the failed drug war, Transform is pleased to announce the Spanish translation (PDF) of our book 'After the War on Drugs: Blueprint for Regulation'. We are also delighted to announce it has a new foreword by César Gaviria, former president of Colombia, and member of the Global Commission on Drug Policy.
Transform's Head of External Affairs, Danny Kushlick, is currently in Mexico, at the III Conferencia Latinoamericana y I Conferencia Mexicana sobre Políticas de Drogas, promoting the translation of the book, along with the new Spanish Count the Costs website and briefings.
We are on the brink of a substantial shift in international drug policy that will transform the entire globe. After decades waiting in the wings, alternatives to prohibition are now firmly on the negotiating table for discussion. The media's overwhelmingly positive response to the report from the Global Commission on Drug Policy, (on which I am proud to have sat as a member), has shown that there is a big appetite for change.
This Spanish translation of 'After the war on Drugs – Blueprint for Regulation' is so relevant now because of the overwhelmingly catastrophic impacts of the war on drugs in Latin America, but also because there now exists here an appetite for progressive reform. In the last year the presidents of both Colombia and Mexico have called for an evidence based discussion of alternatives, including legal regulation.
When it happens, the shift to legal regulation will result in major benefits for states significantly involved in production, transit and consumption of currently illegal drugs. However, the positive repercussions will ripple out far wider and ultimately will bring benefits for all of us – increasing security, promoting development, bolstering democracy and upholding human rights. All of which are close to my heart, from my time as Secretary General of the Organisation of American States, of which these policy principles form the four pillars.
However, it is not all plain sailing. The US has led other UN member states to object to Bolivia's request to amend the UN Single Convention to allow indigenous coca chewing, because they claim it would jeopardise the 'integrity' of the 1961 UN Single Convention on drugs. 'Integrity' implies that the overarching system of drug control is "unimpaired, sound, essential and complete". Every report on drugs from the last ten years shows this to be very far from the truth. None has gone anywhere near giving the current regime a clean bill of health, and in fact most have questioned both its underlying principles and its outcomes, including those from the UN Office on Drugs and Crime itself.
It would be more apt to describe the current system as 'corrupt', in its original sense, meaning rotten and broken. From its conception the UN drug control system has been split in two. In 1961 the Single Convention on Drugs formulated a strict system of regulation on the production, supply and use of drugs for medical use, whilst at the same time constructing an absolute global ban for non-medical production, supply and use.
These control regimes produced two very different markets – one regulated by governments and subject to international trade agreements, the other run by organised criminals, insurgents, paramilitaries and corrupt officials, completely beyond the control of governments or supranational agencies. It is this latter market that afflicts states as far apart as Afghanistan – where the rule of law is significantly undermined by their involvement in opium production, Colombia – whose governance is compromised by the catalysing influence of cocaine in supporting the activities of insurgents, paramilitaries and parapoliticians, Guinea Bissau – where the fragile state collapsed into a narcostate when it became part of the new cocaine transit route, and Mexico – where turf war violence has led to tens of thousands of the most horribly violent deaths in the last few years.
And yet, at the same time, legal cocaine is produced for the medical market, without undermining regional or global security, and opium is grown all over the world for both pain control and for the treatment of addiction, without undermining development in fragile states or contributing to crime and criminality. The only conclusion we can draw from these contrasting examples is that it is the control regime that determines the efficacy of the outcomes. The opium poppies swaying in the breeze in fields in India, Tasmania and the UK will travel a vastly different route than those grown in the fields of Southern Afghanistan.
As this 'Blueprint' demonstrates in some detail, drug regulation is anything but a step into the unknown. The parallel, legally regulated drug market has a long history and a robust evidence base that shows both its strengths and its weaknesses. One lesson we must learn from our experience is that governments must intervene with more vigour in the alcohol and tobacco markets. Multi-national companies that hold profitability as the highest priority are unlikely to hold their customers' health in the highest regard. No one with a serious interest in the needs of the disadvantaged and vulnerable would countenance the free sale of heroin from unlicensed vendors, (as is currently the case for tobacco).
'After the War on Drugs – Blueprint for Regulation' describes three basic options for government regulation – prescription, pharmacy dispensing and licensed sale. Given that the UN estimates that there are up to 250 million users of illegal drugs, it is perverse in the extreme to leave the trade that supplies them, in the hands of organised criminals and unregulated dealers.
The world needs, and deserves, a control regime that is not corrupt, that has genuine integrity – that is sound, complete and whole – that is democratic, and delivers security, development and health within a human rights framework. I am realistic. I believe it could take another ten years to turn this 'Blueprint' into reality. It is a tragedy that in the next decade many thousands more will die using dirty drugs or trying to stop, or fighting over the spoils of, the illegal trade. At the same time we will collectively waste another trillion dollars on the global war on drugs.
But positive steps in the right direction are already underway and public support for reform increases inexorably. Decriminalisation of drug users is gaining ground in several Latin American countries and experiments with the legal regulation of cannabis are almost certain to start in several places within a few years time.
I commend readers of this book to pass it on to politicians who have the power to implement its conclusions. It will prove an invaluable resource for those seeking an exit strategy from the drug war and a solid foundation for peace and security in Latin America and beyond.
Nos encontramos al borde de un significativo cambio en cuanto a políticas internacionales sobre drogas, un cambio que transformará todo el planeta. Tras décadas de esperar su turno por salir a escena, las alternativas a la prohibición se encuentran ahora firmemente colocadas en la mesa de negociaciones para ser discutidas. La respuesta abrumadoramente positiva de los medios de comunicación al informe de la Comisión Global sobre Políticas de Drogas (a la cual me enorgullezco de pertenecer), ha mostrado que existe una enorme sed de cambio.
Esta traducción al español de "Después de la Guerra contra las Drogas – Una Propuesta para la Regulación" es tan relevante ahora debido a los abrumadoramente catastróficos impactos de la guerra contra las drogas en América Latina, pero también porque ahora existe aquí gran entusiasmo por una reforma progresista. Durante el último año, los presidentes tanto de Colombia como de México han formulado llamados por una discusión basada en evidencias sobre estrategias alternativas, incluyendo la regulación legal.
Cuando ocurra el cambio hacia la regulación legal, éste resultará en importantes beneficios para los países que se encuentran involucrados de manera significativa en la producción, tránsito y consumo de drogas actualmente ilegales. Las repercusiones positivas, sin embargo, se expandirán mucho más allá de estos países y, en última instancia, nos beneficiarán a todos – incrementando la seguridad, promoviendo el desarrollo, reafirmando la democracia y ratificando los derechos humanos. Todos estos valores los llevo muy dentro de mí, desde la época en que fui Secretario General de la Organización de Estados Americanos, cuyos cuatro pilares coinciden con estos principios de política.
Éste no será, sin embargo, un recorrido sin tropiezos. Los EE.UU. han llevado a otros Estados miembros de la ONU a objetar la solicitud de Bolivia de enmendar la Convención Única sobre Estupefacientes de las Naciones Unidas para permitir que poblaciones indígenas mastiquen hoja de coca, afirmando que ello amenazaría la "integridad" de la Convención Única de 1961. "Integridad" implica que el sistema global para el control de drogas es "intacto, sólido, esencial y completo". Cada informe sobre drogas de los últimos diez años muestra que ello está muy lejos de ser cierto. Ninguno de estos informes ha llegado al punto de afirmar que el régimen actual está libre de problemas y, de hecho, la mayoría de estos reportes, incluyendo aquellos elaborados por la propia Oficina de las Naciones Unidas sobre las Drogas y el Delito, cuestionan tanto los principios subyacentes como los resultados de este régimen.
Sería más apropiado describir el sistema actual como "corrupto" en el sentido original del término, que significa podrido y dañado. Desde su concepción, el sistema para el control de drogas de la ONU ha estado escindido en dos. En 1961, la Convención Única sobre Estupefacientes formuló un estricto sistema de regulación sobre la producción, suministro y consumo de drogas para uso médico, mientras al mismo tiempo construía una prohibición global absoluta para la producción, suministro y consumo de drogas no médicas.
Estos regímenes de control produjeron dos tipos de mercados muy diferentes – uno regulado por los gobiernos y sujeto a acuerdos comerciales internacionales; y el otro manejado por organizaciones criminales, insurgentes, paramilitares y funcionarios corruptos, ubicado completamente fuera del control de los gobiernos y las agencias supranacionales. Es éste último mercado el que está afligiendo a países tan distantes como Afganistán – donde el Estado de Derecho ha sido significativamente socavado debido a su participación en la producción de opio; Colombia – cuya gobernabilidad está comprometida por la influencia catalizadora de la cocaína en apoyo de las actividades de subversivos, paramilitares y parapolíticos; Guinea Bissau – donde el frágil Estado colapsó para convertirse en un narco-Estado al volverse parte de la nueva ruta para el tránsito de cocaína; y México – donde la violencia de la lucha interna por el control de territorios ha producido decenas de miles de los asesinatos más espantosamente violentos perpetrados en los últimos años.
Y sin embargo, al mismo tiempo, se produce cocaína legal para el mercado médico sin socavar la seguridad regional o global, y se cultiva amapola en todo el planeta tanto para controlar el dolor como para tratar adicciones sin socavar el desarrollo en Estados frágiles ni contribuir a la delincuencia y la criminalidad. La única conclusión que podemos extraer de estos ejemplos contradictorios es que lo que determina la eficacia de los resultados es el régimen de control. El viento que hace balancear los cultivos de amapolas opiáceas en la India, Tasmania y el Reino Unido, les lleva por una ruta diametralmente distinta a la trazada para los cultivos de la misma planta en el sur de Afganistán.
Tal como la presente "Propuesta para la Regulación" demuestra en detalle, la regulación de las drogas no es en absoluto un paso hacia lo desconocido. El mercado paralelo de drogas, legalmente regulado, tiene una larga historia y una sólida base de evidencias que muestra tanto sus fortalezas como sus debilidades. Una lección que debemos recoger de nuestra experiencia es que los gobiernos deben intervenir con más vigor en los mercados de alcohol y tabaco. Es improbable que compañías multinacionales cuya máxima prioridad es la ganancia, tengan mucho respeto por la salud de sus clientes. Ninguna persona seriamente interesada en las necesidades de las personas desfavorecidas y vulnerables toleraría la venta libre de heroína por parte de vendedores sin licencia (tal como ocurre actualmente con el tabaco).
"Después de la Guerra contra las Drogas – Una Propuesta para la Regulación" describe tres opciones básicas para regulación por parte del gobierno – prescripción, venta a través de farmacias y venta con licencia. Dado que la ONU calcula que existen cerca de 250 millones de consumidores de drogas ilegales, resulta extremadamente perverso dejar el comercio que les proporciona estas sustancias en manos de criminales organizados y traficantes exentos de regulación.
El mundo requiere y merece un régimen de control que no sea corrupto, que tenga una genuina integridad – que sea sensato, completo y cabal – que sea democrático, y que ofrezca seguridad, desarrollo y salud en un marco de derechos humanos. Soy realista. Creo que podría tomar otros diez años convertir esta "Propuesta" en realidad. Resulta trágico que en la próxima década muchos miles de personas más deban morir consumiendo drogas sucias o tratando de dejarlas, o disputando el botín del tráfico ilegal. Al mismo tiempo, colectivamente desperdiciaremos otro billón de dólares en la guerra global contra las drogas.
Sin embargo, ya se están dando pasos positivos en la dirección correcta, y el apoyo del público hacia la reforma crece inexorablemente. La descriminalización de los consumidores de drogas está conquistando terreno en varios países de América Latina, y es casi seguro que dentro de pocos años empiecen experimentos con la regulación legal de cannabis en varios lugares.
A quienes lean este libro, les encomiendo que lo hagan llegar a los políticos que tienen la autoridad para implementar sus conclusiones. Este libro resultará ser un recurso invalorable para quienes busquen una estrategia para escapar de la guerra contra las drogas, y una base sólida para lograr la paz y la seguridad en América Latina y en otras latitudes.
Today the War on Drugs: Count the Costs Campaign (La campaña Guerra Contra las Drogas: Calcula los Costos) will be launched in Latin America, alongside the newly translated Count the Costs website and briefings - now available.
Danny Kushlick of Transform Drug Policy Foundation (UK) will outline the many costs of the war on drugs, and the aims of the campaign, followed by a screening of the film Cocaine Unwrapped at the III Conferencia Latinoamericana y I Conferencia Mexicana sobre Políticas de Drogas in Mexico City.
La campaña "La Guerra Contra las Drogas: Calcula los Costos" reunirá a actores involucrados de todo el mundo, incluyendo a OSC, gestores de políticas y otros, cuyo trabajo es impactado negativamente por el tratamiento legal a las drogas. Juntos harán un llamado a los gobiernos y agencias internacionales para evaluar intensamente las consecuencias colaterales de la Guerra contra las drogas y explorar alternativas basadas en evidencias.
La "guerra contra las drogas" en una opción de políticas. Existen otras opciones que deben, como mínimo, ser debatidas y exploradas, valiéndose de las mejores evidencias y análisis que se puedan reunir. Todos compartimos las mismas metas – lograr un mundo más seguro, más saludable y más justo. Por ello, los abajo suscritos hacemos un llamado a los líderes del mundo y las agencias de las NN.UU. para cuantificar las consecuencias negativas no intencionales del enfoque actual hacia las drogas, y evaluar los costos y beneficios potenciales de enfoques alternativos.
Danny Kushlick, hablando en nombre de la campaña Calcula los Costos, dijo: "Tras cincuenta años de guerra contra las drogas, con billones de dólares gastados, las drogas ilegales constituyen una de las mercancías más importantes en el planeta. Incluso la Oficina de las NN.UU. para las Drogas y el Delito, que supervisa en sistema global para el control de drogas, admite que los esfuerzos para aplicación de la ley sobre drogas han alimentado la creación de un vasto mercado criminal con desastrosas consecuencias negativas no intencionales. Ninguna región ha sido más afectada que América Latina.
"Hay algunas contradicciones estructurales fundamentales en esta Guerra contra las Drogas… Nosotros en Colombia hemos sido exitosos, pero este éxito está dañando a toda América Central, México, el Caribe, África, y eventualmente regresará a nosotros. Por lo tanto, ¿estamos asumiendo la política correcta en el largo plazo? No tengo objeción a discutir ninguna de las alternativas, pero si vamos a discutirlas, hagámoslo con todas y cada una de ellas, ¿cuál es el costo?, ¿cuál es el beneficio de cada alternativa?". Presidente Juan Manuel Santos de Colombia, The Washington Post, diciembre de 2010.
La lista de adherentes a la campaña incluye a las siguientes instituciones: International Drug Policy Consortium; Harm Reduction International; Eurasian Harm Reduction Network; Drug Policy Alliance (EE.UU.); Espolea (México); Release (Reino Unido); Fundación Transform Drug Policy (Reino Unido); Sindicato de Libertades Civiles de Hungría; CuPIHD (México); Instituto Transnacional (Países Bajos); International Centre for Science in Drug Policy (Canada); New Zealand Drug Policy Foundation; la Oficina en Washington para Asuntos Latinoamericanos; APDES (Portugal); PIE (Bolivia) y muchas más.
The Count the Costs initiative is a global NGO project calling on Governments and the UN to meaningfully count the many costs of the War on Drugs, and explore alternative approaches that might reduce them.
These costs are divided into seven headings - Development and Security, Public Health, Human Rights, Stigma and Discrimination, Crime, Environment and Economics. Briefings for each area are being produced throughout this year, as well as a growing archive of related reports, videos, images and articles. The Development and Security briefing is already available, as is a summary briefing looking at all seven costs.
Today sees the publication of the next Count the Costs briefing - The War on Drugs: Undermining Human Rights - produced by several project supporters, including Transform, EHRN, Harm Reduction International, and Release.
"In every region of the world the war on drugs is severely undermining human rights. It has led to a litany of abuse, neglect and political scapegoating through the erosion of civil liberties and fair trial standards; the denial of economic and social rights; the demonising of individuals and groups; and the imposition of abusive and inhuman punishments.
Too often these human rights violations are considered in isolation – a drug user beaten by police to extract information; a drug courier executed by firing squad; a family killed at a military checkpoint; an HIV worker imprisoned for distributing harm reduction information; a family displaced by aerial fumigation of their crops; a drug user detained for years of forced labour and beatings on the recommendation of a police officer; a cancer sufferer denied pain-killing medicine. But they are not isolated. They are all a direct consequence of the war on drugs.
Like the war on terror, the war on drugs is framed as a response to an exceptional, existential threat to our health, our security, and indeed the very fabric of society. The "Addiction to narcotic drugs" is portrayed as an "evil" the international community has a moral duty to "combat" because it is a "danger of incalculable gravity" that warrants a series of (otherwise publicly unacceptable) extraordinary measures. This is not an exaggeration of the political rhetoric. These words are enshrined in international law, including the 1961, 1971 and 1988 UN drug conventions.This crusading language has created a political climate in which drug war policy and enforcement are not required to meet human rights norms.
In fact, despite being one of the three pillars of the UN's work (along with development and security), these international agreements lack any obligation to ensure compliance with human rights. In over one hundred articles, human rights appear specifically only once (in relation to crop eradication)(4) – a staggering omission in treaties negotiated and adopted post-World War II, in the era of the modern human rights movement. This omission is now reflected in national law and policy worldwide. Through production, transit, sales and use, the responses to every stage in the supply chain of illicit drugs are characterised by extensive human rights violations, committed in the name of supply and demand reduction.
In order to meaningfully count these human rights costs, it is necessary to not only see the connections between law and policy, and the effects on the ground, but also to make comparisons with what happens under alternative approaches, including the decriminalisation of the possession of drugs, and models of legal regulation. For example, most of the abuses resulting from a punitive, enforcement-led approach to illegal drugs do not occur in relation to the production, sale and use of tobacco, alcohol and prescription medicines.
Ultimately, just as UN member states refer to "shared responsibility" for drug control, so too must they bear shared responsibility for human rights abuses perpetrated in its name. That is what Count the Costs is about – taking responsibility and openly evaluating all policy impacts, and all other options."


Kumaratunga Munidasa was born on the 25th of July 1887 in Dickwella, Matara District, Sri Lanka. He was the son of Palavinnage Dona Gimara Muthukumarana (also known as Dona Baba Nona Muthukumarana) (mother) and Abious Kumaranatunga (father). He was born as the 12th son, in a family of 13 children. Munidasa was a famous Sri Lankan poet, linguist, commentator, grammarian, journalist, and writer. He is considered as one of the most prominent Sri Lankan scholars the country has seen in several centuries.
At the beginning he entered the Buddhist School in Dikwalle. Later he attended the St. Thomas' College in Matara. After that he entered the Dikwella Watarukanna Pirivena to learn Sanskrit and Pali in order to become a Buddhist monk. However, due to the dissatisfaction of his family, Kumaratunga Munidasa left Dikwella Watarukanna Pirivena and attended the Government Teachers' College that was in Colombo in 1905. He graduated in 1907, after 2 years of training.
Kumaratunga Munidasa wrote "Nikaya Sangraha Vivaranaya", his first book, while he was still is school. He wrote many books in many different languages such as Sinhala, English, Sanskrit, Pali, Tamil, Greek, Malayalam and Latin. Munidasa revived the "Lakminipahana", one of the oldest Sinhala newspapers, from 1934 to 1936. He also started 2 magazines named Helio and Subasa (Good Language) in 1939, to promote and teach the correct way of using the Sinhala language.
In 1941 Kumaratunga Munidasa founded the "Hela Havula" (Pure Sinhala Fraternity), a Sinhala Literature Organization. The organization consisted of many people who shared Munidasa's idioms. They frequently engaged in debates and centered about many aspects of literature. Hela Havula is considered as a starting point for many Sri Lankan artists and scholars. The organization is still active and helps many young artists to sharpen their literary skills.
Munidasa always used to point out Country, Language, and Nation as his Triple Gem, and also stressed the importance of Sinhala language, as the emblem of national identity of the Sinhalese People. He was awarded the "Viwarna Swakthi" name as an admiration to his excellent work on the history of Sinhala literature.
Munidasa Kumaratunga passed away on 2nd of March 1944 at the age of 56.


These classic Native American earrings have two sterling silver feathers handmade by Harvey Chavez of the Kewa Pueblo tribe in Northern New Mexico, USA. Earrings measure 4.5cm from the top of the ear wire to the tip of the longest feather. Each feather is signed with the makers mark.
Harvey Chavez makes exquisite feather jewellery. Each feather is entirely handmade, skilfully formed from sterling silver, each unique in size, shape and detail. Every line is individually stamped using a homemade stamp and each feather is carefully shaped, polished and signed. As well as feather necklaces Harvey and his wife Janie make feather bracelets, rings, feather earrings, feather pendants, brooches, hair clips and bolo ties.


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Solving SpaceNet Road Detection Challenge With Deep Learning
By Jonathan Howe, May Casterline and Abel Brown | February 20, 2018
Tags: Deep Learning, Object Detection
It's that time again — SpaceNet raised the bar in their third challenge to detect road-networks in overhead imagery around the world. Today, map features such as roads, building footprints, and points of interest are primarily created through manual techniques. In the third SpaceNet challenge, competitors were tasked with finding automated methods for extracting map-ready road networks from high-resolution satellite imagery. This move towards automated extraction of road networks will help bring innovation to computer vision methodologies applied to high-resolution satellite imagery and ultimately help create better maps where they are needed most such as humanitarian efforts, disaster response, and operations. For more details, check out the SpaceNet data repository on AWS and see our previous NVIDIA Developer Blog post on past SpaceNet challenges to extract building footprints.
In this post, we approach the current SpaceNet challenge from distinct perspectives. The first part of this blog describes how to directly leverage the full 8-band imagery and manipulate ground truth labels to obtain excellent road networks with relative ease and excellent performance. We next look at how we might exploit the material properties of the road surface itself by using the spectral aspect of the data to create a deep learning solution tailored for a specific spectral signature. Finally, we take creative liberties to think about how we might apply these types of deep learning solutions in a broader operational sense using conditional random fields, percolation theory, and reinforcement learning. Think of this like Bohemian Rhapsody for deep learning (minus the Grammy Hall of Fame).
Section 1 – Tiramisu and Manipulating the Truth
In this section the Tiramisu network is used which is readily available in many forms on GitHub. Here we use the Semantic Segmentation Suite, written by George Seif, which incorporates many different segmentation methods. This network was chosen because it can provide highly accurate semantic segmentation masks for a variety of segmentation tasks and easily extends to the full 8-channel data (in contrast to just 3-channel RGB data). From a design perspective, Tiramisu extends the U-Net architecture adding Densely Connected Convolutional Networks into the network through conversion into fully convolutional layers to enable upsampling. The advantages of these modifications include deeper supervision between layers. Given that the feature maps are shared throughout the dense blocks this aids multi-scale supervision and introduces skip connections within and outside of each block, a feature shown to be extremely successful in Residual Networks.
Figure 1: The Tiramisu architecture. Image credit: Jégou et al, arXiv:1611.09326
To infer road networks using the SpaceNet data a number of preprocessing steps are required to create segmentation masks for training and evaluation. The tool sets provided by Cosmiq Works provide useful methods to convert from the line string graph formats into a segmentation mask allowing the user to specify the width of the segmented road. It is possible to generate a segmentation model with these tools, however it is evident upon visual inspection that these segmentation masks do not label all pixels which are associated with roads, highways and alleyways. For example a segment may either be too wide or too narrow to describe a specific road. In the case of a label being too wide this results in buildings, trees and other objects to be labelled incorrectly as road. Conversely, a too narrow label will result in potentially large areas of the road network being labelled as the background class. In some cases areas which clearly contain roads are not labelled as such. This label mixing can adversely affect accuracy, particularly at large intersections which, as described in the Cosmiq Works blog, are extremely important to label correctly to maximize the Average Path Length Similarity (APLS) metric.
Label Pre-Processing
To reduce this label misassignment we can use relatively straightforward image processing approaches. Whilst these approaches will not completely rectify the problem they will increase the number of pixels correctly labelled as a drivable surface and background classes. To achieve this the OpenCV floodFill function is employed. From the OpenCV website:
The functions floodFill fill a connected component starting from the seed point with the specified color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The pixel at (x,y) is considered to belong to the repainted domain if:
The floodFill function effectively generates a mask for all connected points which fulfil the above statement. Using this method it is possible to use a thinned version of the segmentation mask created by the Cosmiq Works functions to specify seed points for the floodFill function. Examples of the Las Vegas dataset processed using the Cosmiq Works and the floodFill methods are presented below in figure 2 (left: image, middle: Cosmiq Works boundary method, right: floodFill segmentation). It is clear that many more pixels are correctly labelled, particularly in parking lots, cul-de-sacs, and major trunk roads. In addition objects on or overhanging roads such as vehicles and vegetation are no longer labelled incorrectly.
Figure 2: Vegas image 1035, examples of the Cosmiq Works mask generator and our floodFill approach
The Tiramisu network is trained using Tensorflow on the full 8 band Vegas AOI dataset. The Cosmiq Works mask tool and the floodFill method are used for label generation and model accuracy is calculated using mean Intersection over Union (IoU) for both road and background classes and using the APLS metric. In all cases the networks were trained for 14 epochs taking approximately 6 hours per model per dataset per GPU when using NVIDIA Quadro GP100 GPUs. When using 8 band data the number of initial convolutional filters in the Tiramisu network is increased from 64 to 96 to increase the network capacity to learn multispectral features. The proportion of training, validation and test data is 70%, 15% and 15% respectively.
Figure 3 shows the resulting masks from this process using the Tiramisu network on hold out test data. The left image presents the ground truth mask and graph, middle predicted mask and graph using the Cosmiq Work's boundary function, and the right image shows predicted mask and graph using the floodFill approach. The floodFill is clearly superior when compared to the boundary fill method. The wide highway and it's junctions are detected which is advantageous when calculating APLS. However care must be taken in areas where floodFill selects areas which are clearly drivable, such as parking lots and side roads, which are not labelled in the ground truth images.
Figure 3: Vegas image 865, inferred masks and graphs using 6 meter and the floodFill approach.
The Tiramisu network trained on all 8 bands achieved a very respectable mean IoU of 0.89 and mean APLS of 0.60. This was achieved through simple image preprocessing and postprocessing steps and by training a standard open source model against the full dimensionality of the dataset without any major changes or additions.
In this section we have discussed some image processing methods to aid data preparation which can significantly increase accuracy in some cases. Data labelling and formatting is at the heart of data science and artificial intelligence. Depending on the overall goals of your requirements the correct selection of data preprocessing methods, and selecting the most relevant set of evaluation metrics, can help you achieve them.
In the next section we focus on determining the best spectral content to use when training a deep learning network for a specific spectral signature.
Section 2: Focusing the Spectrum
In this section we want to think about the physical properties of the road materials we are trying to isolate as well as the surrounding observable environment and how the sensor's characteristics might be able to help in the road extraction task. That is, we want to exploit material-specific spectral signatures within the imagery for efficient asset detection and localization. The SpaceNet road challenge data were collected by the DigitalGlobe Worldview-3 satellite. The 8-band multispectral images contain spectral bands for coastal blue, blue, green, yellow, red, red edge, near infrared 1 (NIR1) and near infrared 2 (NIR2) (with corresponding center wavelengths of 427, 478, 546, 608, 659, 724, 833 and 949 nm, respectively). This extended spectral range allows Worldview-3 imagery to be used to classify the actual material that is being imaged as shown in Figure 4 .
Figure 4: Spectral bands for DigitalGlobe's Worldview 3 sensor
Spectral features for a given material can be thought of physical characteristics of the material that manifest as observed reflectance (or absorbance) changes at particular wavelengths. Traditional spectral analysis leverages these diagnostic features to perform material identification. For example, organic materials such as vegetation exhibit very distinct spectral features in the red and near-infrared (NIR) portion of the spectrum. These features are driven largely by the chlorophyll content of vegetation. Chlorophyll will strongly absorb energy in the visible portion of the spectrum and strongly reflect energy beginning in the red/near-infrared (wavelengths ~700 nm and longer).
Given that we are interested in roads, why do we care about vegetation? As you can see from Figure 5, asphalt tends to be a poor reflector in the red and near-infrared portions of the spectrum. In fact, man-made objects in general will exhibit this poor reflectance characteristic. These differences will manifest as a strong contrast between vegetation and manmade objects in the last three spectral bands of the Worldview 3 sensor which is easily exploited with deep learning.
Figure 5: Spectral effects of asphalt aging and deterioration (Herold)
Of course, this is not an exhaustive spectral analysis of all environments observed in the SpaceNet data but it does motivate the investigation of a pure R/NIR deep learning model. The additional caveat here is that this data has not been compensated for atmospheric effects and that has the potential to affect results. For the sake of simplicity, we will assume that the atmospheric properties are constant over the entire Las Vegas AOI. That is, the atmosphere distorts all AOI-2 images in the same way. And, since Las Vegas is a dry desert-like climate, it is reasonable to assume minimal atmospheric distortion.
Something else to keep in mind is that the bit depth of the imagery from satellite sensors is often higher than 8-bit. However, the majority of deep learning frameworks and generic image manipulation libraries cannot handle data with higher bit depths. In combination with tools such as numpy, one can convert the data down to the required 8-bit depth and use it in subsequent steps. The sample code below provides two methods for extracting band information and using numpy and scipy's bytescale function to perform a contrast stretch on the raw data that clips the top 10% and bottom 2% of values.
def retrieve_bands(ds, x_size, y_size, bands):
stack = np.zeros([x_size, y_size, len(bands)])
for i, band in enumerate(bands):
src_band = ds.GetRasterBand(band)
band_arr = src_band.ReadAsArray()
stack[:, :, i] = band_arr
return stack
def contrast_stretch(np_img, p1_clip=2, p2_clip=90):
x, y, bands = np_img.shape
return_stack = np.zeros([x, y, bands], dtype=np.uint8)
for b in range(bands):
cur_b = np_img[:, :, b]
p1_pix, p2_pix = np.percentile(cur_b, (p1_clip, p2_clip))
return_stack[:, :, b] = bytescale(exposure.rescale_intensity(cur_b, out_range=(p1_pix, p2_pix)))
return return_stack
Segmenting road networks from R/NIR data
The last 3 bands of the MSI pan-sharpened imagery were extracted from the original imagery for AOI-2 in Las Vegas (i.e. red edge, NIR1, and NIR2). Each image was resized to 1024×1024 and scaled to 8-bits with a contrast stretch clips of 2% and 90%. Ground truth masks did not make use of any additional pre-processing (i.e. flood fill). Training and validation datasets were created with a typical 20% split. A sample R/NIR training image is show below. It is interesting to note the pan-sharpening artifacts manifesting as blur in the vegetation, namely trees.
Figure 6: Sample R/NIR training image from AOI-2 in Las Vegas
The same Tiramisu architecture (described in section 1) was used again for this exercise. Since we are using a fully convolutional network (FCN) we are not restricted by input size at inference, however it does need to be a multiple of the original training dimensions. For this effort a training size of 512×512 was chosen. Conveniently, Geospatial Data Abstraction Library (GDAL) includes method calls to resize geospatial datasets while preserving the geographic metadata via a warping function. For added functionality, this can either be done in a file or in memory, depending on where you chose to insert this process in your workflow. The sample code below shows the GDAL API call used to do a resize in memory.
warp_ds = gdal.Warp('', img_fn, format='MEM', width=resize[0],
height=resize[1],
resampleAlg=gdal.GRIORA_NearestNeighbour, outputType=gdal.GDT_Byte)
The model was trained for ~50 epochs with a batch size of 1 on the R/NIR data for Vegas. Shown below are some samples from the segmentation results generated by the model.
Figure 7: Original R/NIR imagery from Vegas AOI, provided ground truth segmentation, predicted segmentation mask
The segmentation results were processed using some custom tools and the provided APIs and tools to extract a road network (represented by a graph) and calculate the APLS score per image. Below are the companion road network predictions for the presented samples.
Figure 9: Extracted road network comparison from R/NIR imagery. Predicted (right), Ground truth (left)
Figure 10: Extracted road network comparison from R/NIR imagery. Predicted (right), Ground truth (left)
The model trained on only the R/NIR data produced a mean IoU of 0.71 and a mean APLS score of 0.56
Above we discussed using a subset of the spectral data to train a deep learning model for road extraction. While there are a few implicit assumptions in this approach (e.g. atmospheric corrections), this method significantly reduces the amount of data needed for model training by focusing on a specific spectral signature. Compared to the full 8-band model, these R/NIR model results are slightly degraded but potentially more desirable given the data reduction and elimination of pre-processing overhead (i.e. flood-fill). Although, the minor performance reduction could simply be due to the fact that this approach uses a lot less data!
In the next section we are going to go off the beaten path and think about things slightly differently.
Section 3: Thinking Outside the Box
In this section we leverage the NVIDIA GPU Cloud resources together with the next-generation of AWS EC2 compute-optimized GPU instances. The AWS P3 instances are powered by up to 8 of the latest-generation NVIDIA Tesla V100 GPUs and are ideal for computationally advanced workloads such as machine learning (ML), high performance computing (HPC), data compression, and cryptography. Based on NVIDIA's latest Volta architecture, each Tesla V100 GPU provides up to 125 TOPS of mixed-precision performance, 15.7 TFLOPS of single precision (FP32) performance and 7.8 TFLOPS of double precision (FP64) performance. For machine learning and deep learning applications, the P3 instance offers up to 14x performance improvement over previous P2 instances based on the much older NVIDIA Tesla K80 GPUs, allowing developers to train their machine learning models in hours rather than days.
For road segmentation we utilize the awesome Mask R-CNN deep learning network architecture implemented by Matterport available on GitHub. Mask R-CNN is a flexible framework for object instance segmentation which efficiently detects objects in an image while concurrently generating high-quality segmentation masks for each instance. To do this, Mask R-CNN extends Faster R-CNN by adding an additional branch for predicting an object mask together with the original branch for bounding boxes.
Figure 11: Mask R-CNN extends two existing Faster R-CNN heads adding an additional branch for predicting an object mask. Image credit: He et al, arXiv:1703.06870v3
For training Mask R-CNN we start with the model sufficiently trained in advance on MS COCO data. The network is configured with 128 training ROIs per image, RPN anchor scales of (8, 16, 32, 64, 128), and an RGB pan-sharpened input image size of 512x512x3. Over all four areas of interest in the SpaceNet dataset there are 2731 total training images with associated ground truth road labels. These data were divided into a 90/10 split of 2458 images used to further train the network and 273 holdout images for validation. A training image label consists of an arbitrary number of road lines each specified as list of points in pixel space. Bresenham's line algorithm was used convert these lines to binary segmentation mask labels for network training. It is important to note that Mask R-CNN is an object detection network at heart and therefore each road line must be treated as a separate object mask rather than combining all road lines into a single binary mask of size 512×512. That is, if a training image specified N road lines then the associated training label for that image would be a binary mask of size 512x512xN where label[:,:,i] is the binary mask for the i'th road line. The network used here was trained with a batch size of 8 images for 40 total epoch with 300 steps per epoch.
A traditional approach for working with object detectors is to threshold detections scores (confidence metric from 0 to 1) at 0.5 so that detections scoring less than 0.5 are discarded. Similarly, the segmentation mask produced by the Mask R-CNN network provides a softmax probability for each image pixel as "road" or "not-road". Those pixels scoring, say, 0.87 have high probability of being road while a pixel score of 0.21 would be a weak indication of "road". Therefore, a common post-processing approach is to binarize the resulting segmentation mask by converting all softmax probabilities below 0.5 to 0 and all probabilities 0.5 and above to 1.
Another popular approach to aid in the pixel classification decision processes is a technique called conditional random fields (CRFs) which is often employed for "structured prediction" in pattern recognition and hence readily applied in semantic segmentation post-processing. Essentially, a CRF is a probabilistic graphical model that encodes known relationships between observations and produces interpretations consistent with those relationships. A discrete classifier such as threshold classifier described above, on the other hand, predicts a label for a single sample without considering "neighboring" samples while a CRF can take context into account. It is crucially important to view the deep learning network as a technique for producing information that informs a decision process rather than a device that makes a decision.
Here are a few Mask R-CNN results with the aforementioned post-processing approaches:
Figure 12: Holdout validation image 1517 from Las Vegas (AOI-2). Here we show the combined binary ground truth label for all road lines as well as the softmax pixel probabilities inferred by the trained Mask R-CNN model. Finally, both the threshold mask and CRF post-processing techniques are shows side-by-side.
Figure 13: Holdout validation image 383 from Khartoum (AOI-5) with associated ground truth, network segmentation mask, and post-processing techniques
Figure 14: Results and post-processing of holdout validation image 220 from Paris (AOI-3)
In this section we want to highlight how we might work with and apply this data for real operations. At this point, given some overhead image we can produce an "educated" guess for where roads might be within that image. So, now what? For starters, given a map of interconnected roads we can start to ask basic questions like "can we get from A to B?" or similarly "what are all the traversable locations from location X?" Just because two points are "connected" does not inform us for exactly how we might actually go about getting from point A to B. Notice that creating a simple binary test (yes/no) for
bool = is_connected(mask,A,B)
is quite computationally efficient compared to something like "find the optimal path from A to B". This raises another question, given some road segmentation mask, what is meant by "optimal path" between two points? In routing applications we often think of "optimal" as "shortest" and/or "fastest" but here we also have to consider that we're not entirely certain where the road actually is. We can therefore start to think about things like the maximum likelihood route between two points (hint: it is not likely going to be the shortest route). Again, this underpins the notion that the deep learning network as a technique for producing information that informs a decision process.
If we think of an image as a grid of vertices with edges between vertices (i.e. pixels) with probability p, the description is strikingly similar to a domain of statistical physics called percolation theory. In general, percolation refers to the movement of fluids through porous materials and over the last decades the theory of percolation has brought new understanding and techniques to a broad range of topics in physics, materials science, complex networks, epidemiology, and other fields. A famous problem in percolation theory involves systems comprised of randomly distributed insulating and metallic materials: what fraction of the materials need to be metallic so that the composite system is an electrical conductor? If a current passes through the system from top to bottom then the system is said to percolate. In such a system a site (i.e. grid point) is "open" or "vacant" with probability p and "blocked" with probability q = (1-p).
Image credit: Princeton COS 226
What makes this problem famous is the system phase change that occurs above a critical vacancy probability Pc. That is, systems with site vacancy below the critical threshold, p < Pc, almost never percolate and systems with p > Pc almost always percolate.
Since the segmentation mask provides a probability for each pixel we can think of each pixel as a Bernoulli random variable. That is, think of each pixel as a biased coin that when flipped produces heads with probability p and talis with the "leftover" probability of q = (1-p) — just like the site percolation problem above. In this way we create a binary (0 or 1) mask where each pixel is assigned heads = "road" = 1 or tails = "not-road" = 0. Once we have a random road network realization we can grow (i.e. percolate) connected components. Below shows a few examples of using the segmentation softmax probabilities to produce random realization of the road network and their associated connected components. For any two points in a connected component, there exists a path between those points.
Figure 15: Using the segmentation softmax probabilities we can create random realization of the associated road network. Each realization is equally probable and could be used for Monte Carlo simulation, for example.
Figure 16: From a random realization of the road network, we can grow connected components. For any two points in a connected component, there exists a path between those points. This is a good example where the result is a large connected component. Repeat the analysis with additional realizations to build a confidence interval.
Figure 17: Notice the "fuzzy" regions of road where connectivity is poor)
Figure 18: We try to grow connected components. Notice that the random road network realization produces fragmented components (i.e. island of connectivity). There is no path between points is different components.
Routing versus Navigation
Percolation was a useful way to investigate connected components and build a deeper understanding for connectivity from a probabilistic perspective which provided a simple yet robust formalism for testing if two points were connected. However, from an operations standpoint, given some objective or goal we really need to use the information provided for routing purposes. That is, how do we move beyond probabilistic connectivity to more useful capabilities such as optimal navigation of the environment? Given some graph of vertices and edges we can, no doubt, compute the shortest path between points. Although, if we consider an image with dimensions 512×512 pixels that translates to a graph with 266,144 vertices which means shortest path computations will scale poorly as the environment size increases. For example, Dijkstra's algorithm has worst case performance of O(|E| + |V|log(|V|)) where |E| is the number of edges and |V| is the number of vertices and for grid graph of size {h,w} we have |V| = n*m and |E| has a maximum possible value of (n-1)*m + (m-1)*n. With the native image resolution of 1300×1300, the SpaceNet roads challenge data produces graphs with 1,690,000 vertices and 3,377,400 possible edges. These numbers quickly become intractable for 4k and 8k overhead imagery. Operationally, given some goal location, it can be quite computationally expensive to determine shortest path from an arbitrary location on-the-fly. Furthermore, we still need to incorporate the environmental uncertainty of where the road actually is! Another option to consider is the A* algorithm (pronounce "A star") which generally achieves better performance by using heuristics (i.e. environmental informance in this case) to guide the search process. The A* algorithm is an informed best-first search process where the search considers the vertices that appear to lead most quickly to the goal. While A* can incorporate environmental information to the search process and thus typically converge faster to a path between two points, there are still some things to think about. For example, these routing approaches work well if we know exactly the starting point to the goal. However, if for some reason we find ourselves in a totally different location on the map we then need a new route computed to the goal. One might naively think to precompute routes to the goal from all possible starting locations but that is a lot of routes to take in your back pocket as you parachute out of an airplane. Perhaps rather than explicit routes what we need is a way to navigate the uncertain environment from wherever our starting point ends up to be.
Gridworld 2049
Rather than worry about computing explicit routes between all possible starting location and the goal (an intractable solution at any rate) we can simply leverage reinforcement learning to generate an optimal action from each location that leads to the goal. This type of solution allows for goal navigation from any location on the map which accounts for all the environmental uncertainty up front. At the core, reinforcement learning (RL) is a dressed up Markov decision process (MDP) which is itself a form of discrete-time stochastic control process. Here MDPs provide a mathematical framework for modeling decision making in environments where outcomes are partly random and loosely under the control of a decision maker. MDPs are widely used in optimization problems and solved using dynamic programing (DP) and Bellman's equation. For more RL content and discussion see previous NVIDIA Developer Blog posts (RL nutshell and OpenAI Gym). Just as MNIST is the iconic deep learning exercise, Gridworld is the classic RL example.
Figure 19: Classic Gridworld environment where there are four possible actions {up,down,left,right} from each grid location. The task is to determine the optimal action for all possible states/locations that move the agent towards the end goal state. Image credit: Towards Data Science
For our application of RL, we will assign some goal state on the road and use the softmax probabilities produced by Mask R-CNN to define the transition probabilities. In a traditional gridworld, there are only four possible actions {up, down, left, right} so that is what we will use for our navigation example here. Notice that even if the neighboring grid location has high value, we discount that value if it leads to a non-traversable location (i.e. low probability of being road). Using simple policy iteration approach (Sutton and Barto, Ch 4) we can determine high-value actions at each grid point which lead to the goal state. One can almost think of this as a coarse vector field that illuminates a force pulling agents in the environment towards to the goal. As a simple example, in Figure 20 below, we consider image 442 from AOI-5 in Khartoum with a goal state in an intersection near the top of the image marked with a large red dot. After a few hundred iterations the optimal policy begins to emerge with clear direction to the goal state (Figure 21 below). Notice that we now have an optimal action for each pixel. Even if the pixel is not a road surface, the policy prescribes the action which leads to the nearest road (i.e. high value areas) and from there to the goal state (obtain maximum reward).
Figure 20: Example environment in Khartoum for reinforcement learning proof-of-concept for goal navigation. The RL policy solution assigns the optimal action for each pixel location which leads to the goal state marked with a big red dot near the top of the image)
MDPs and RL techniques provide a powerful framework for decision making under uncertainty. Furthermore, these methods can easily accommodate additional environmental information such as buildings (not traversable, must navigate around) and utility functions (i.e. how painful are type I and type II errors), so forth and so on. Additionally, simple policy iteration methods exhibit massive parallelism and can be quickly updated on-the-fly as new information becomes available in an operations or situational awareness type scenario. And finally, this type of decision framework extends naturally to more complex state and reward descriptions to methods such as DeepQ learning (deepRL) and Monte Carlo search trees which led to the historic AlphaGo championship win.
Figure 21: Gridworld derived from image 442 in AOI-5 Khartoum. The optimal policy to the goal state (red dot) derived from the road segmentation mask for local grid points (30×30 pixel region).
Summary and Closing Comments
In this blog we have presented various methods to model and infer road networks to predict routes between locations within the SpaceNet road detection dataset. This includes experimenting with preprocessing methods to increase model accuracy, using domain knowledge to select the most appropriate spectral bands for a given target of interest, and combining percolation theory and reinforcement learning techniques to calculate navigable and efficient paths directly from imagery.
To enable this we utilized high performance GPU compute resources provided by the NVIDIA GPU Cloud (NGC) and AWS. Using pre-configured NGC framework containers and AMIs eliminated many hours (or days) of setup and provided optimized performance-tuned deep learning frameworks. The Amazon P3 instances provided low-cost easy access to state-of-the-art GPUs for fast network training in just a few hours using the latest NVIDIA Tesla V100 GPUs. Just think, a few years ago this same work would have taken days to configure, tune, debug, and train while costing thousands of dollars for enterprise hardware access. Today with NGC and AWS, we can knock out proof of concept work like this in just hours for around $50. Join us at our GPU Technology Conference 2018 for more great content and the latest in deep learning and artificial intelligence! Use code CMGOV to receive a 25% discount on your GTC registration. See you there!
About Jonathan Howe
Dr. Jonathan Howe (PhD Physics, University of York, UK) has 12 years experience as a computer vision, data analysis, and physical modeling scientist and engineer at in the UK's Ministry of Defence (MoD). He has developed many tools using machine learning and optimization methods for both the US and UK governments particularly in the Remote Sensing, ISR, Intelligence, and Security domains. He was selected as one of the UK government's leading experts in deep learning and machine learning, winner of the MoD Chief Scientific Adviser award, recipient of the prestigious Arnold Award for outstanding applied research, and awarded an operational service medal for his field contributions while embedded within a US Processing Exploitation and Dissemination Cell in Afghanistan. Dr. Howe created and led the UK MoD's Kaggle competition to detect and segment objects and scenes in WorldView 3 datasets. Dr. Howe joined the NVIDIA federal solution architecture team in 2017 specializing in deep learning and artificial intelligence for defense and intelligence applications.
View all posts by Jonathan Howe
About May Casterline
Dr. May Casterline is an image scientist and software developer with a background in satellite and airborne imaging systems. Her research interests include deep learning, hyperspectral and multispectral imaging, innovative applications of machine learning approaches to remote sensing data, multimodal data fusion, data workflow design, high performance computing applications, and creative software solutions to challenging geospatial problems. She holds a PhD and Bachelors of Science in Imaging Science from Rochester Institute of Technology, with a focus on remote sensing. In industry she has acted as a product owner, technical lead, lead developer, and image scientist on both research initiatives and development projects. Dr. Casterline joined the NVIDIA federal solution architecture team in 2017 focusing on deep learning and artificial intelligence applications.
View all posts by May Casterline
About Abel Brown
Dr. Abel Brown is a deep learning solution architect with NVIDIA. Abel helps the world's leading technology companies and research institutions develop deep learning solutions to tackle the biggest challenges in defense, intelligence, healthcare, and autonomous driving. Prior to joining NVIDIA, Abel led defense research with DARPA and launched science satellites with NASA. He holds degrees in physics and mathematics.
View all posts by Abel Brown
Exploring the SpaceNet Dataset Using DIGITS
By Jon Barker and Allison Gray | August 31, 2016
Deep Learning for Object Detection with DIGITS
By Jon Barker and Shashank Prasanna | August 11, 2016
Creating an Object Detection Pipeline for GPUs
By Prethvi Kashinkunti and Floris Chabert | June 19, 2019
NVDLA Deep Learning Inference Compiler is Now Open Source
By Rekha Mukund, Prashant Gaikwad and Mitch Harwell | September 11, 2019

redpajama_set_name: RedPajamaCommonCrawl


Java Runtime Environment (32-bit) for Windows. Review - Espresso Runtime Atmosphere (JRE) permits you to play on-line video games, chat along with folks around the world, determine your mortgage loan passion, and also perspective pictures in 3D, only to name a few. This is actually likewise indispensable to the intranet applications and also other e-business answers that are the base of corporate processing.
The Java Runtime Atmosphere (JRE) gives the collections, the Java Virtual Maker, and also other components to operate applets and also applications written in the Caffeine computer programming foreign language. Furthermore, two release modern technologies become part of the JRE: Coffee Plug-in, which Makes it possible for Applets to Run in Popular Browsers; and Coffee Web Beginning, which sets up standalone applications over a system. A lot of ratty system uses additionally call for Java to function effectively.
Java Runtime Environment (32-bit) for Windows - 's Caffeine is actually a technology used on all significant systems (Microsoft window, Mac computer, Linux) to make sure a secure workplace for several uses. Platform transportability is just one of its primary perks and because of this, nearly every part from digital devices worldwide makes use of Coffee technologies. Billions from PCs, mobile phones, Blu-ray gamers, printers, health care devices, auto navigation devices or even webcams produce our team of this.
Coffee permits designers to make software application on one platform as well as provide that to consumers working other platforms. Hence, an application established on Windows could successfully operate on Macintosh or even Linux as well as vice versa. Caffeine Runtime Setting permits the implementation of almost any program, coming from video games to web applications and also solutions.
Java Runtime Environment (JRE) combines a digital maker (likewise called the Espresso Virtual Maker), a collection of primary courses and also surrounding system libraries. Actually, JRE embodies a runtime atmosphere for the virtual device and is actually the part that enables customers to run.jar treatments (Espresso archive, exclusive Espresso format). In the course of setup, Caffeine Runtime Setting likewise releases a plugin that helps with the implementation from applets within regional browsers.
While Java sits with patience on the computer system, this immediately takes action when called upon. And that happens regularly than you may consider. Whether you participate in on the web video games or conversation along with your friends, Java is running in the history seeing to it you have a delightful encounter.
's system likewise provides a Control Panel that makes it possible for the configuration from network and also temporary Net file setups or even security certificates. You could also handle the setups from the JRE and also accessibility advanced functions including Debugging, Coffee console, Default Caffeine for Browsers, Java Plug-in, JavaFX runtime, JRE Auto-Download or even Surveillance.
The Coffee Runtime Atmosphere is actually also ensured to become dispersed by developers along with their principal Espresso treatment. By doing this, they make certain that you are going to have the ability to manage their Java-written programs. That is made use of unconfined range on numerous gadgets as well as has met an usability level to the point where possessing that is certainly not extra any longer.
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For Renault, the electric vehicle is ultimately the real answer to the current problems related to the environment.
With ZOE, Renault puts Zero Emission mobility within the reach of many. Revealed at 2012 Geneva Motor Show, Renault ZOE will be sold at the end of the year.
Discover the Renault Z.E. website.
Renault takes into account the overall impact of its vehicles at each stage of their life: design / manufacture, use and recycling.
Objective of this comprehensive approach "life cycle" to the automobile leaves fewer traces on Earth.


If kids are the future, then it makes sense to start targeting them now with political propaganda. The Utah Division of Oil, Gas and Mining recently held an Earth Day poster contest for elementary school children with the theme, "Where would we be without oil, gas and mining?" The contest was sponsored by the Society of Petroleum Engineers. And, gee, this is the second year running for the contest, which the division spokesman says is meant to "improve awareness" of the important role that oil, gas and mining play. And he noted that alternative energy won't be able to meet the demand for energy in the future. Well, with that attitude, it certainly won't. Meanwhile, Utah Moms for Clean Air is sponsoring an alternative poster contest: "Explore the economic, environmental and health costs of fossil fuels on Utah." Color them mad.
While the Division of Oil, Gas and Mining is shining its spotlight on the wonders of fossil fuels, it might just want to turn its attention to safety and maintenance. Chevron Pipe Line Company is at it again, trying to clean up yet another oil spill from an aging pipeline. This time, it was Willard Bay taking on 27,000 gallons of diesel. The Salt Lake Tribune writes that The National Response Center has reported 293 Utah pipeline incidents over 23 years, and 44 of those were Chevron's. The company has had to pay millions in fines and cleanup costs, and has been trying to sell off its 760-mile pipeline. It's no doubt difficult to monitor the old pipeline, but Chevron might consider putting its money upfront for maintenance instead of waiting for disaster.
Utah GOP Chairman Thomas Wright wants to explore changing the state's caucus system or risk a statewide initiative to make primaries more inclusive. Indeed, the GOP caucuses have fallen into the hands of the right wing and ousted popular incumbents like Bob Bennett and Olene Walker. Delegate elections are stacked by the far right, maybe because no normal conservative wants to deal with the vitriol. But the funniest part of this effort is the charge that initiative-backer LaVarr Webb is wealthy enough to make it happen. Hey, initiatives in Utah are almost impossible, and the Legislature is always up for making them more difficult. Not even former U.S. Rep. Merrill Cook, R-Utah, and his former millions could push some of them forward. But, oddly, Cook is lobbying against the initiative now, saying it's up for the highest bid. Is someone paying for signatures?


text:
Adam O'Connor
IT Professional interested in Ruby on Rails, DevOps, Horizontal Team Leadership, and Full Stack Development
I live with my girls (wife, daughter, and pug) in Defiance, Ohio.
Currently using Ruby, Ruby on Rails, Sinatra, JavaScript (intermediate), REST, API, XML, JSON, CSS, MySQL, RDS, EC2, ELB, Route53, Git, Heroku, VIM, tmux, tmuxinator
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This page is an overview of who I am, my experience as an IT professional, and values that mean a lot to me.
As a youngster I started coding as a hobby on the Commodore 64, 128, Amiga, Apple II c, e, gs. This interest led me to code by trade since 1996. Throughout my career I have worked for corporate, collegiate, and government institutions through roles ranging from developer to project and organizational management.
The majority of projects I've worked on have been start-ups. My primary talent is taking ideas and turning them into full production stacks.
In addition to my profession I am graduate of the University of North Carolina at Chapel Hill and a proud U.S. Navy veteran.
Kindness & Respect In regards to the people I work with - what matters most to me is treating each other with kindness and respect. I have learned that communicating and understanding each other makes work fun and productive.
Stability I am dedicated and therefore only want to work for stable companies, teams, & projects. Like you, I have responsibilities and it can be distracting having to worry about providing vs. being focused on a cool feature.
Transparency I like to see and understand how things work. By the same token my confidence is stronger when I know everyone involved is on the same page. Transparency allows a group to think as one.
Results Oriented Adult day care frustrates me. Unfortunately its very commonplace. This is a topic that I am very fond. I believe in a ROWE and have not found a more productive environment.
Swich.com - CTO 2019-Present
Swich is our new adventure. It's a culinary wellness app that is built around a choose your own path concept. We help people by where they want to go from a culinary perspective. It's really cool because the more a person goes down their path the more we're able to help them by recommending techniques, recipes, and groups to support them on their journey. This, like Rouxbe, is another high nutrition movement as we really are helping people improve their lives. I'm especially excited about this one because we have an awesome team and a very modern stack. Since this was a green pasture project, we have been able to pick the best tech for each feature.
Rouxbe.com - CTO 2014-Present
This is my current home and the newest addition to my family. Rouxbe is the web's dominate online cooking school with an emphasis on wellness. I love working at Rouxbe not just for the people I work with but for the subject matter and how it helps people. This is the first company I've worked for where the focus is on a person's quality of life vs. profit. Even though I've been at Rouxbe for a while now I still can't shake that honeymoon feeling of working for a fresh company and new material.
Contactability.com - IC 2011-2013
I love Contactability because it is a SaaS currently for the lead generation industry. This was a fun platform to build because of that excitement one gets from working on a green pasture project. While getting Contactability off the ground I lived on-site Monday-Thursdays and slept on a cot in a cubicle camp I setup (remember I said I was dedicated).
InsuranceAgents.com - IC 2011-2012
I was an individual contributor for InsuranceAgents.com mainly helping out with their Ruby apps. IA was a mix of Perl and Ruby - the former holding a large backend presence. IA was a big data company with a lot of high performance systems that required the highest reliability - minutes of being down were strongly felt. IA did really well and was eventually acquired by Bankrate.com. By the time of the acquisition I had already been working on a new project that would become the subsequent start-up - Contactability.com.
Trail10.com - Founder 2010-Present
After leaving MT I formed up Trail10 to organize my contracting career. Trail10 has led me to some cool people and projects. I mainly work on light to medium size coding projects with Trail10 depending on what my full time responsibilities and work load is.
MachineTools.com - CTO 2005-2011
MT brought me back into the start-up world. It was stable, active, and a family. At first the subject matter was foreign to me but eventually got into it. We underwent a large project of migrating a 10yo monster stack into Ruby on Rails and launched without a hitch. I really grew at MT and eventually became the CTO. As CTO of MachineTools.com I managed an on shore team as well as off shore teams in India and Russia which worked on a side startup project.
Government & Corporate Gigs - Developer 2001-2005
The .com bubble popped which tossed me into school => military => corporate life.
SoulIntegration.com - Head of Engineering 1997-2001
Artificial Intelligence was the focus at Soul Integration. We primarily created custom intranets but also specialized in the automation of whatever we could get our hands on (store, construction company, automotive inventory, etc.).
IOS Communications - Developer 1996-1997
I built and maintained web sites for IOS which was one of the town's ISPs. IOS was unique because we used an early proprietary hypertext preprocessor which allowed us to create complex dynamic web sites, CMS, and web apps.
Hosted on GitHub Pages — Theme by orderedlist


text:
Home Health All stay in Mexico – Trump includes asylum-Deal with the Mexican President
All stay in Mexico – Trump includes asylum-Deal with the Mexican President
The United States and the future of the Mexican government have agreed, according to a report on an Asylum system. The "the Washington Post reported" citing high-ranking members of the transition team of the incoming President, Andrés Manuel López Obrado, the on 1. December sworn in. Accordingly, asylum-seekers in Mexico must wait while their cases by U.S. courts will be examined. US President, Donald Trump tweeted: "migrants at the southern border to enter the country in the United States, if their claims are approved to court individually."
He added: "We will only allow those who come legally in our country." Otherwise, our very strong policy of Grabbing and Verhaftens attack"". There will be no "release" in the United States. "We will all stay in Mexico." Once again, Trump threatened with the closure of the border to Mexico. "If it is for some reason necessary, we will close our southern border. Never, the United States will accept after decades of abuse, this costly and dangerous Situation!"
Migrants at the Southern Border will not be allowed into the United States until their claims are individually approved in court. We only want to allow those who come into our Country legally. Other than that our very strong policy is Catch and Detain. No "Releasing" into the U. S…
— Donald J. Trump (@realDonaldTrump) 24. November 2018,
….All want to stay in Mexico. If for any reason it becomes necessary, we will CLOSE our Southern Border. There is no way that the United States wants, after decades of abuse, put up with this costly and dangerous situation anymore!
The scheme would make it Central American migrants is significantly harder to reach the United States. The designated Mexican interior Minister Olga Sánchez Cordero said the "Washington Post", you have agreed to the "policy of remaining" in Mexico, this is a short-term solution, the medium – and long-term solution is that the people emigrated from their homeland.
Manuel López Obrador is the New President of Mexico: The Anti-Trump, the real Trump is happy
Humanitarian emergency in Tijuana
In the Mexican border town of Tijuana to be housed in a stadium currently, more than 4700 Central American migrants. They come mostly from Honduras, El Salvador and Guatemala – and hope for political asylum in the United States. Thousands more Central Americans Mexican border town in the North on the move – fleeing violence and poverty in their home countries. It is estimated that a total of around 10,000 people want to go to Tijuana. There is a "humanitarian emergency" has now been revoked.
A U.S. court was stopped last week by Trump had tightening of asylum rules to the southern border to Mexico temporarily. Thus, such people can apply again for asylum in the United States, have crossed the border illegally. The corresponding injunction will remain at least until a hearing on may 19. December. The President had on 9. November a proclamation was signed, according to the migrants crossing the southern border into the United States illegally, the asylum procedure is denied. The procedure should be, in principle, only at official border crossings.
Trump had claimed, among the migrants, many Criminals and "terribly dangerous people". Documents he delivered. He had ordered several Thousand soldiers to the southern border, to provide logistical support to the border protection authorities.
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Literature – Lou wrote to Apollinaire
Düsseldorf's coach about Bayern Defensive: "very Very vulnerable"
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Study trip in the Norwegian Wilderness: So it must be to live without a Smartphone and Smalltalk


It's all come down to this: Two of disc golf's favorites have rolled into the final showdown for our Roller of the Year.
Nate Sexton. Eagle McMahon. Your votes. We'll close the polls late Thursday night and announce the winner on Friday. Until then, go nuts!
Thanks to Jomez Productions and The SpinTV for the clips. Let's roll!
Out of all of his tremendous rollers in this competition, Sexton's aggressive play at the United States Disc Golf Championship has taken out the most contenders. It was one of the key shots on the way to his title, but will it be enough to claim this one?
Comments on "2017 Roller Of The Year Presented By Friction Gloves: Finals"


text:
Osama Was My Dreamboat
Editor, Quadrant Online
On the western flood plain of the Maribyrnong, the lesser of Melbourne's two brown rivers, Buddhists have built themselves a handsome temple and, most arresting, a gigantic golden statue of their guiding philosophy's founder (left). It is quite the spectacle and well worth a glance as your Werribee-bound train approaches Footscray station. But unless you have a particular interest in the sound of one hand clapping, a glance is all it's worth — and, obligingly, Buddhists don't see any need for grants and government programs to promote "understanding" of their creed. Alas that another religion were so content to mind its own business. As Fairfax Media demonstrates today with a series of profiles — Australia's Muslims Speak Up — it seems that one cannot be regarded as a fair and unbiased citizen without an obligatory knowledge of Islam, its adherents, their agonies and the bigotry we are told yet again makes the lives of Australia's faithful so very difficult.
That, at any rate, is the series' intent. The end result, however, is the polar opposite. Unwittingly, wrapped in its gush of multi-culti pablum, at least one of the profiles illustrates why one doesn't need to be a peddler of prejudice to find Islam more than somewhat alien and not a little unsettling.
The subject of this extended plea for understanding and empathy is 32-year-old Aisha Novakovich, described as "community advocate, law student and mother of two". Her first-person story, ghost-written by Fairfax's Beau Donelly, professes to be an account of "Islamophobia and anti-Muslim discrimination in Australia" by one who has been its victim. This is where, like so many Fairfax stories, a hefty measure of cognitive dissonance is required to accept the politically correct premise.
Those lacking the gift for believing that which is at at odds with the evidence will be appalled, to put it mildly, by Ms Novakovich's personal history, the intolerance and contempt she displayed as a youth for others and, perhaps worst of all, the tokenist pandering of political leaders and community-award distributors who pointedly overlooked the obvious when hailing her as a herald of the future to which all Australians must aspire. But let Ms Novakovich tell her story in her own words. This how it begins:
As a teenager, I had a laminated photo of Osama Bin Laden in my bedroom. A friend once saw the photo and demanded to know why I had it. I told her he was my hero and that I wanted to be one of his wives; that the media was manipulating his image because he was challenging US hegemony.
From the age of 14, I wore the niqab – the full face veil – with gloves. My mother hated it and said I was becoming a fanatic. For me, it became a marker of difference in the face of discrimination. At university I was determined to learn the political systems in the West in order to subvert and challenge them from within. I believed we needed a caliphate. Eventually, I started to flirt with ways I could leave Australia to join the fight against foreign oppressors…
Now here is the interesting bit. At the same time Ms Novakovich was getting about covered head-to-foot, yearning to take a number and await the amorous attentions of the heroic bin Laden, she was being feted by politicians who did not have a clue what the recipient of their attentions looked like, let alone believed. No worries! Here was a Muslim who could be useful in papering over then-recent memories of her dreamboat hero's attacks on the Twin Towers and Pentagon. Why not sling her a trophy salad bowl while using the occasion to lecture the greater public about its knuckle-dragging surmise that radical Islam might just have something to do with, you know, Islam. Below is a photo of her accepting Western Australia's inaugural Minister's Multicultural Ambassadors Youth Award in 2002.
It doesn't stretch the imagination too much to imagine the minister departing that ceremony for a gathering of feminists, where it is even easier to picture him uttering further hollow pieties, this time against the oppression of women and the imposition of male standards on their dress, assigned roles and comportment. As a fully paid-up member of the Cliche of the Week Club, he would have been every bit as disinclined to recognise that contradiction as to begin searching for traces of breakfast in the beard of fellow award recipient and just-now declared Labor Senate candidate Pat Dodson, who can be seen hovering in the background.
Ms Novakovich's accolade for her efforts "to increase awareness about the Muslim community" is worth reading. And do bear in mind that, at the same time she was "involved in Police Cadet training", she was an unabashed admirer of the man whose disciples murdered some 3000 innocent souls for a morning's work. The award citation concludes by noting that "Aisha is the Muslim and Youth representative of the State Government's Anti-Racism Strategy Steering Committee" who "seeks to raise awareness of Muslim culture among the wider community." Not, presumably, by being candid about her admiration for a Koranically motivated mass murderer.
But wait, that's not all! Ms Novakovich's ghost-writer, Fauxfax's young Beau Donelly, now inspiring the approbation of his fellow Fairfaxians, grasps that Muslims must always be presented as victims, no matter what. As his employer no longer has any editors worthy of the title, this means his text can feature a glaring contradiction without fear of being summoned to the news editor's desk and asked if he is as stupid as his copy suggests. Here is Ms Novakovich's account of her experience upon being removed from a secular, mainstream state school and enrolled in an Islamic one:
I had always thought of myself as an Aussie girl with Aussie friends at an Aussie school. The world I lived in was very much part of mainstream Australia and that's where I felt a strong sense of belonging.
I was bitterly disappointed when I was sent to an Islamic high school. The facilities were run down and staff were unprofessional. The kids were unwelcoming, unlike the non-Muslim students I had grown up with. I felt like I'd been catapulted into an environment that stifled creative expression and contained pockets of deep puritanical ideology.
Just to be clear, her days in the allegedly intolerant belly of Australian society were happy. Her days in an educational Islamic enclave were not. Yet toward the end of the piece we are told:
Growing up in the 1990s, I saw a lot of graffiti that screamed "Asians out" or "Asian invasion", which made me conscious that I was not white and therefore not welcome.
So Ms Novakovich was "happy in the mainstream" and "felt a strong sense of belonging" yet simultaneously found herself tormented by the thought that she "not white and therefore not welcome." Go figure that one.
Or better yet, the next time she is in line for the award of a multi-culti Mixmaster or a set of halal flying ducks, let us hope the glad-handers of the intolerance industry also invite a saffroned sampling of those Maribyrnong Buddhists. While they are unlikely to evince much enthusiasm for the Islamic concept of taqiya — presenting Islamic belief as other than it is in order to gain advantage — they might demonstrate by example that a proven way to lead an untroubled life in Australia is to forego the awards and pleasures of dubious victimhood and all the attention that goes with it.
That, and not keeping a kissable photo of Osama bin laden on your bedroom wall.
Roger Franklin is the editor of Quadrant Online.
Lawrie Ayres – 21st April 2016
Australia has gone mad and there is a group, growing larger it seems, that is hell bent on making us madder. Since no politician has had the brains or the courage to challenge this rubbish we must assume they are not worth voting for regardless their party.
[email protected] – 22nd April 2016
There is the Australian Liberty Alliance Lawrie. Check it out. They are aiming for senate seats only at this stage but it's a start in the right direction.
pgang – 22nd April 2016
The ALA is not a start in the right direction. These people want to control Australia via the Senate. That makes them no better than the rest of the unelected swill who think they have the right to over-rule democratic process. It doesn't matter whose side they claim to be on.
ron.house – 22nd April 2016
Also, read their constitution. Members have no say in anything. The leaders select their replacements. IOW, the members are there solely to satisfy the number requirements of a political party. These are the characteristics of a front organisation.
[email protected] – 21st April 2016
Do not suppose (I a sure you don't) that Australia is alone in this. The whole of the ruling class in Europe toes the line on this one. The people are a differemt matter, but hell, who listens to them?
This frankly disturbing article points to the need for all voters in the upcoming federal election to scrutinise their local candidates with great care. Any candidate who has actively contributed to the promotion of this alien and malign presence in our society must be voted down accordingly.
Look beyond party labels and instead at the candidate's own beliefs, words and actions. Most parties have some decent candidates, and most also harbour traitors to their country.
A sister-in-law is visiting from Switzerland at the moment and says she has noticed a big change in Australians since two years ago. There is a lot more negativity and worry. But hey, this is the most exciting time ever to be an Australian.
jenkins – 22nd April 2016
I hope I am allowed to post this on the Quadrant website, but I think it is sympathetic to the views of many Quadrant readers.
Here is a petition to end social justice courses at University:
https://www.change.org/p/universities-suspend-social-justice-in-universities?recruiter=308794977&utm_source=share_petition&utm_medium=copylink
Please sign and pass around if you agree.
Here is a video of the creator of the petition explaining why it is important:
https://www.youtube.com/watch?v=78ucO8XOw3c
Harry Howden, Heir to Hornblower
Hal G.P. Colebatch
A Case for Immigration Reform
Clive James: The Screwing of Les Murray


MIL-DTL-22759/84 wires are large size, high-performance aerospace hookup wires with a four-layer insulation of PTFE tape and PTFE/ polyimide/PTFE tape, with an outer layer of polyamide braid for superior abrasion resistance. These wires are designed for aerospace and other applications requiring excellent thermal stability and abrasion resistance.
W884-4/0 Conductor Size N/A 4/0 AWG Min. Conductor Dia. N/A 0.565 in14.35 mm Max. Conductor Dia. N/A 0.605 in15.37 mm Stranding N/A 2109/30 Min. Insulation Diameter N/A 0.615 in15.62 mm Max. Resistance per 1000 ft. @ 20°C N/A 0.056 ohms Temperature Rating N/A -55 to 260 ºC Weight N/A 661.4 lb/1000 ft.984.00 kg/1000 m.
W884-3/0 Conductor Size N/A 3/0 AWG Min. Conductor Dia. N/A 0.500 in12.70 mm Max. Conductor Dia. N/A 0.540 in13.72 mm Stranding N/A 1665/30 Min. Insulation Diameter N/A 0.554 in14.07 mm Max. Resistance per 1000 ft. @ 20°C N/A 0.071 ohms Temperature Rating N/A -55 to 260 ºC Weight N/A 520.3 lb/1000 ft.774.10 kg/1000 m.
W884-2/0 Conductor Size N/A 2/0 AWG Min. Conductor Dia. N/A 0.440 in11.18 mm Max. Conductor Dia. N/A 0.475 in12.07 mm Stranding N/A 1330/30 Min. Insulation Diameter N/A 0.498 in12.65 mm Max. Resistance per 1000 ft. @ 20°C N/A 0.089 ohms Temperature Rating N/A -55 to 260 ºC Weight N/A 414.7 lb/1000 ft.617.00 kg/1000 m.
W884-1/0 Conductor Size N/A 0 AWG Min. Conductor Dia. N/A 0.395 in10.03 mm Max. Conductor Dia. N/A 0.425 in10.80 mm Stranding N/A 1045/30 Min. Insulation Diameter N/A 0.442 in11.23 mm Max. Resistance per 1000 ft. @ 20°C N/A 0.113 ohms Temperature Rating N/A -55 to 260 ºC Weight N/A 337.0 lb/1000 ft.501.40 kg/1000 m.
W884-1 Conductor Size N/A 1 AWG Min. Conductor Dia. N/A 0.366 in9.30 mm Max. Conductor Dia. N/A 0.380 in9.65 mm Stranding N/A 817/30 Min. Insulation Diameter N/A 0.400 in10.16 mm Max. Resistance per 1000 ft. @ 20°C N/A 0.144 ohms Temperature Rating N/A -55 to 260 ºC Weight N/A 282.0 lb/1000 ft.419.60 kg/1000 m.
W884-2 Conductor Size N/A 2 AWG Min. Conductor Dia. N/A 0.320 in8.13 mm Max. Conductor Dia. N/A 0.340 in8.64 mm Stranding N/A 665/30 Min. Insulation Diameter N/A 0.360 in9.14 mm Max. Resistance per 1000 ft. @ 20°C N/A 0.177 ohms Temperature Rating N/A -55 to 260 ºC Weight N/A 217.9 lb/1000 ft.324.20 kg/1000 m.


text:
package org.bioconductor.packages.caMachineLearning.service.globus;

import org.bioconductor.packages.caMachineLearning.service.CaMachineLearningImpl;

import java.rmi.RemoteException;

/**
 * DO NOT EDIT:  This class is autogenerated!
 *
 * This class implements each method in the portType of the service.  Each method call represented
 * in the port type will be then mapped into the unwrapped implementation which the user provides
 * in the CaMachineLearningImpl class.  This class handles the boxing and unboxing of each method call
 * so that it can be correclty mapped in the unboxed interface that the developer has designed and
 * has implemented.  Authorization callbacks are automatically made for each method based
 * on each methods authorization requirements.
 *
 * @created by Introduce Toolkit version 1.2
 *
 */
public class CaMachineLearningProviderImpl{

	CaMachineLearningImpl impl;

	public CaMachineLearningProviderImpl() throws RemoteException {
		impl = new CaMachineLearningImpl();
	}

    public org.bioconductor.packages.caMachineLearning.stubs.LearnResponse learn(org.bioconductor.packages.caMachineLearning.stubs.LearnRequest params) throws RemoteException {
    org.bioconductor.packages.caMachineLearning.stubs.LearnResponse boxedResult = new org.bioconductor.packages.caMachineLearning.stubs.LearnResponse();
    boxedResult.setMachineLearningResultCollection(impl.learn(params.getSingleChannelExpressionDataCollection().getSingleChannelExpressionDataCollection(),params.getCagridMachineLearningParameters().getMachineLearningParameters()));
    return boxedResult;
  }

    public org.bioconductor.packages.caMachineLearning.stubs.CreateCaMachineLearningSessionResponse createCaMachineLearningSession(org.bioconductor.packages.caMachineLearning.stubs.CreateCaMachineLearningSessionRequest params) throws RemoteException {
    org.bioconductor.packages.caMachineLearning.stubs.CreateCaMachineLearningSessionResponse boxedResult = new org.bioconductor.packages.caMachineLearning.stubs.CreateCaMachineLearningSessionResponse();
    boxedResult.setSessionIdentifier(impl.createCaMachineLearningSession());
    return boxedResult;
  }

    public org.bioconductor.packages.caMachineLearning.stubs.InvokeLearnResponse invokeLearn(org.bioconductor.packages.caMachineLearning.stubs.InvokeLearnRequest params) throws RemoteException {
    org.bioconductor.packages.caMachineLearning.stubs.InvokeLearnResponse boxedResult = new org.bioconductor.packages.caMachineLearning.stubs.InvokeLearnResponse();
    impl.invokeLearn(params.getSessionIdentifier().getSessionIdentifier(),params.getCagridMachineLearningParameters().getMachineLearningParameters());
    return boxedResult;
  }

    public org.bioconductor.packages.caMachineLearning.stubs.GetStatusResponse getStatus(org.bioconductor.packages.caMachineLearning.stubs.GetStatusRequest params) throws RemoteException {
    org.bioconductor.packages.caMachineLearning.stubs.GetStatusResponse boxedResult = new org.bioconductor.packages.caMachineLearning.stubs.GetStatusResponse();
    boxedResult.setStatus(impl.getStatus(params.getSessionIdentifier().getSessionIdentifier()));
    return boxedResult;
  }

    public org.bioconductor.packages.caMachineLearning.stubs.GetRpackageSessionInfoResponse getRpackageSessionInfo(org.bioconductor.packages.caMachineLearning.stubs.GetRpackageSessionInfoRequest params) throws RemoteException {
    org.bioconductor.packages.caMachineLearning.stubs.GetRpackageSessionInfoResponse boxedResult = new org.bioconductor.packages.caMachineLearning.stubs.GetRpackageSessionInfoResponse();
    boxedResult.setResponse(impl.getRpackageSessionInfo());
    return boxedResult;
  }

}

redpajama_set_name: RedPajamaGithub


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