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Southglenn CO Auto Loans
Home/Colorado Auto Loans/Southglenn CO Auto Loans
Southglenn CO Auto LoansAllCreditCarLoans2019-10-30T20:31:07+00:00
Southglenn CO Auto Loans For All Credit Situations
Looking For Car Finance In Southglenn CO During November 2020?
Getting the best deal on a car loan in Southglenn CO can save you a significant amount of money over the life of your loan. That's why you need a partner who knows how to get you approved for the vehicle that you want at the lowest car loan interest rate possible and with no hassle.
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By working with AllCreditCarLoans, you can get preapproved for a car loan before going to the car dealership, so that you can negotiate as a cash buyer and get the best deal possible.
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Watch Our Video On Getting A Car Loan In Southglenn CO
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Use our car payment calculator to determine how much you can spend when refinancing or financing your next car. You can run different scenarios by varying the "down payment" and "number of months" fields in order to arrive at your desired payment.
Your total car expenses should be no more than 20% of your take-home pay.
What To Know Before You Apply For An Auto Loan
Car Loan Credit Score
Car Loan Interest Rates
Car Loan Terms
Auto Loan Restrictions
Steps To Get An Auto Loan
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1 - Budget For Your Auto Purchase
The first step in obtaining auto financing is to figure out how much you can afford to spend.
If you have a car to trade-in, you should determine its value so that you can factor that into your budget. A good resource for determining your auto's market value is Kelley Blue Book.
Finally, use our vehicle finance calculator to estimate your monthly payment. You can vary the interest rate and loan term to see how that affects the potential monthly payment.
2 - Choose Whether You Want A New Or Used Vehicle
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Let us help you get preapproved for that loan you want and you will become a cash buyer. This saves you time at the dealership and gives you the power to negotiate your best deal on any vehicle you choose. Apply for a new car loan in Southglenn CO and see how much we can save you.
If you choose to purchase a used vehicle, you can click here to view the inventory of used car dealerships in Southglenn CO.
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Auto dealers usually offer car financing through their preferred lenders, typically at a higher interest rate than available elsewhere. Getting preapproved directly with one of our lending partners helps you to negotiate the best car loan rate before you even get to the dealership so that you can save money in the long run.
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When you're applying with us, the application process is simple and quick. You should have the following information on-hand:
About Southglenn CO
Southglenn is a former census-designated place (CDP) in Arapahoe County, Colorado, United States. The population was 43,520 at the 2000 census. It was built as a master planned community by Jordon Perlmutter. It has been a part of the city of Centennial since the city's incorporation in 2001.
Southglenn is located at 39°35′30″N 104°57′25″W / 39.591735°N 104.956963°W / 39.591735; -104.956963Coordinates: 39°35′30″N 104°57′25″W / 39.591735°N 104.956963°W / 39.591735; -104.956963.
According to the United States Census Bureau, the CDP had a total area of 9.7 square miles (25 km), all land.
As of the census of 2000, there were 43,520 people, 16,436 households, and 12,359 families residing in the CDP. The population density was 4,467.4 per square mile (1,725.2/km²). There were 16,694 housing units at an average density of 1,713.7 per square mile (661.8/km²). The racial makeup of the CDP was 94.60% White, 0.79% African American, 0.40% Native American, 1.83% Asian, 0.06% Pacific Islander, 0.86% from other races, and 1.45% from two or more races. Hispanic or Latino of any race were 4.04% of the population.
There were 16,436 households out of which 35.3% had children under the age of 18 living with them, 63.6% were married couples living together, 8.7% had a female householder with no husband present, and 24.8% were non-families. 20.1% of all households were made up of individuals and 6.4% had someone living alone who was 65 years of age or older. The average household size was 2.62 and the average family size was 3.03.
In the CDP, the population was spread out with 26.1% under the age of 18, 5.6% from 18 to 24, 26.7% from 25 to 44, 29.4% from 45 to 64, and 12.2% who were 65 years of age or older. The median age was 41 years. For every 100 females, there were 93.4 males. For every 100 females age 18 and over, there were 89.1 males.
The median income for a household in the CDP was $69,833, and the median income for a family was $77,405. Males had a median income of $53,282 versus $35,754 for females. The per capita income for the CDP was $32,699. About 1.4% of families and 2.6% of the population were below the poverty line, including 2.7% of those under age 18 and 2.8% of those age 65 or over.
For more information, see Southglenn Colorado wiki
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Fox Business Network GOP Debate Tonight at 9pm ET
By Nate | Debate News, Republican Primary Debates | November 10, 2015
The Fox Business Network (FBN) will be hosting the fourth Republican Presidential Debate tonight from Milwaukee, Wisconsin. The debate is being moderated by Neil Cavuto, Maria Bartiromo, and Gerard Baker. The main debate will take place at 9pm Et, 6pm PT. There will also be an earlier undercard debate at 7pm ET, 4pm PT. Chris Christie and Mike Huckabee did not make the primetime cut and will appear in the earlier debate, while George Pataki and Lindsey Graham did not even make the cut for the earlier debate and will not be on stage at all tonight.
Fox Business/WSJ Republican Debate
Airing On: Fox Business Network
Live Stream: FoxBusiness.com (free)
Watch the Full 9pm Video
Candidates: Trump, Carson, Rubio, Bush, Fiorina, Cruz, Kasich, Paul
Candidates: Christie, Huckabee, Jindal, Santorum
Moderators: Neil Cavuto, Maria Bartiromo, and Gerard Baker
Report from the Milwaukee Journal Sentinel:
The moderators of Tuesday's Republican presidential debate in Milwaukee are hoping to deliver something serious but not dull, tough but not snarky, compelling but not stagy.
"I'm not interested in a fight. I'm interested in a conversation about moving the needle on growth, about moving the needle on jobs," says Maria Bartiromo, Fox Business Network anchor.
"This will be the fourth debate and we still don't have a clear idea how these guys plan to deal with the most important issue on voters' minds — and that is jobs," she says.
After a firestorm over the last GOP debate on CNBC on Oct. 28, the rival Fox financial network hopes to put its own stamp on the debate season.
The network has run ads jabbing CNBC for not asking "the real questions" and is promising to deliver Tuesday "the real debate on our economy."
Fox Business anchor Neil Cavuto says "there were lot of very fine questions in (the CNBC) debate" but that viewers didn't feel like they got the meaty economic discussion they expected.
"It quickly morphed into something that maybe was not intended. That happens," he says. "It's about getting back to what made debates really good, the candidates mixing it up on issues they're very passionate about, and with questions that don't goad them but get to the core of what they're all about."
Cavuto, Bartiromo and Wall Street Journal editor-in-chief Gerard Baker are the panelists for Tuesday's 8 p.m. (Milwaukee time) debate, sponsored by Fox Business Network and Baker's newspaper.
This debate will be streamed free at FoxBusiness.com, no cable or satellite subscription needed. Furthermore, Fox Business Network is making itself available in most "basic" lineups as a free preview for tonight's event so you may be able to watch it on your television even if you don't normally get FBN. Check their channel finder to confirm.
As always, we'll have the complete video following the debate for both the primetime and the undercard events.
Tagged in 2016 presidential election fox business network november 10 republican debate republican primary wisconsin
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redpajama_set_name: RedPajamaCommonCrawl


These gages come in the form of a plug and they are usually used to measure diameters of holes. These pre-sized precision metal plugs are specifically designed to determine the acceptability of diameters of machined or drilled holes. Depending on its class, plain plug gages may have an inch tolerance of .0001", .0002", etc. In most cases, these plugs should be used in a GO, NOGO combination.
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Bengaluru based research firm gives 5-star rating to Udaipur-based Urban Square Mall
Jul 24, 2020 | Industry And Cluster, News & Insights
New Delhi – Leading developer Bhumika Group's retail project in Udaipur, Urban Square mall, gets a 5-star rating from Bengaluru-based IBE (International Brand Equity) research firm. Rajasthan-based developer was rated as the best upcoming mall developer in Northern India and IBE critically assessed brand rating and customer satisfaction index based on various parameters such as Architectural Design, Quality of construction, Timely Project Delivery, Customer satisfaction, and Vendor satisfaction. The delivery of Urban Square is progressing as per delivery timelines and the project will be delivered as per RERA commitment. Designed by Award-winning international architect Bentel from South Africa, it is one of the only few projects where the construction is progressing at Udaipur.
The Group has already signed up various international brands in its projects Urban Square such as Shoppers Stop, Reliance Trends, Inox, Marks & Spencer, Holiday Inn, Adidas, Puma, Asics, Zudio, Aeropostale, Sketchers, US Polo, Arrow Sport, FCUK, F-Bar, Looks, Go Colors, Reebok, Pepe Jeans, among others. These brands have leased around 1.75 lakh sq ft area.
Urban Square is a 1.8 million sq ft mixed-use project being developed in 2 phases, which also has a mall positioned as an 'All Under One Roof' with lifestyle retail, along with a food court, a state-of-the-art multiplex, gaming zone, a bowling alley, and many more entertainment options. The development also includes Grade-A commercial office space, commercial serviced suites, and a 5-star hotel. Commenting on the rating, Uddhav Poddar, Managing Director, Bhumika Group said, "It is a result of our efforts towards providing top-of-the-class infrastructure. We are delighted that our efforts are being recognized and this is an assurance to all the stakeholders that the Group has been working in the right direction. It is because of this positive image that we were able to close some new leases during Covid."
Last year, the Group was adjudged as the Emerging Developer of the year – Shopping Malls at the ET Now Star of the Industry Awards for Excellence in Real Estate sector. The Group also bagged 3 more awards in 2019 for outstanding work and contribution in real estate sector – 2nd runner at Images Retails Awards for being most admired upcoming Shopping Centre of the year for its project Urban Square; the Best mixed-use project of the year at 11th Realty+ Conclave and Excellence Awards 2019; and "Emerging Developer of the Year – Retail/Shopping Mall" at DNA presents Real Estate & Infrastructure Awards, Mumbai. In addition to these achievements, Urban Square has also been conferred with The Asia Pacific Property Awards in the Mixed-Use, Retail & Design categories by the International Property Awards, UK.
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Route Exchange Community News
2020: A summer- summary:)
Posted 15 January 2020 14:12
By Athens International Airport S.A. - Eleftherios Venizelos
New Destinations, Services, and more Airlines in Athens!
Allegiant Announces Service from Pittsburgh to Three Markets
By Pittsburgh International Airport
Allegiant now serves 15 nonstop destinations from Pittsburgh International Airport.
New record at Brussels Airport with 26.4 million passengers in 2019 in conjunction with fewer flights
By Brussels Airport
In 2019, Brussels Airport hit a new passenger traffic record with 26.4 million passengers. Cargo transport closed the year at 667 thousand tonnes,...
Munich Airport reports 10th consecutive record-setting year
By Munich Airport
Passenger figures up by 4 percent in 2019 to a total of approximately 48 Million Munich outperforms all German airports in...
CPH traffic data: Close to 30.3 million passengers in 2019
By Copenhagen Airports A/S (CPH)
With close to 30.3 million passengers in 2019, Copenhagen Airport matched the previous traffic record set in 2018. The year also produced the...
More passengers and over CHF 600 million in commercial turnover for the first time
By Zurich Airport
In 2019, 31.5 million passengers passed through Zurich Airport, corresponding to an increase of 1.3%. The number of flight movements was -1.1%...
The Milan Airports 2019 traffic results: Milano Malpensa and Milano Linate
By SEA Milan Airports
At Malpensa the year 2019 ends with excellent passenger traffic results: the traffic volume structurally amounted to 26.8 million passengers at...
London : The most popular destination from Eindhoven Airport, again
By Eindhoven Airport
London is still by far the most popular destination for travelers from Eindhoven Airport.
Pittsburgh Gains New Nonstop to Cabo on Vacation Express
Vacation Express will operate the first-ever nonstop flight from Pittsburgh to Los Cabos International Airport.
Qatar Airways announces service from Lyon Airport
By Lyon Airport
Lyon Airport, part of the VINCI Airports group, will welcome Qatar Airways in Lyon from 23 June 2020
Limited passenger growth and reduced number of flights in 2019
By Avinor Oslo Airport
A total of 54 million passengers travelled through Avinor's airports in 2019, which is an increase of 0.6 per cent compared to the previous year.
Super Weekend 2020 at AUA!
By Aruba Airport Authority N.V.
Aruba Airport Authority N.V. (AAA) opened the New Year with all hands on deck to handle over 24,000 passengers who traveled over the 2-day period,...
New building project planned on western side of Zurich Airport
Plans are in place for a new building development in the western part of Zurich Airport to meet the future demand for business aviation hangar...
Tenth year of Traffic growth at MLA
By Malta International Airport
7 million passenger milestone reached in 2019!
A record year for Edinburgh Airport Passenger numbers increase by 3.1%.
By Edinburgh Airport
A total of 14,747,830 people travelled through Edinburgh Airport last year, making 2019 the busiest in the airport's history and the busiest ever...
Venezia Airport is the official hub of the National Football Team
By Venezia Marco Polo Airport
Venezia Airoirt the Save Group and the Italian Football Federation have presented the agreement of official sponsorship of the Italian...


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El Palacio Municipal de Congresos del Campo de las Naciones de Madrid, en España, fue inaugurado en 1993. Se encuentra junto a las instalaciones feriales del Ifema (Feria de Madrid).

Fue diseñado por Ricardo Bofill Levi.

Características
Su moderno diseño, propio de las obras de ese arquitecto, y una superficie útil de más de  repartidos entre sus 14 plantas (7 de ellas subterráneas), le otorgan una gran versatilidad, con la tecnología y los servicios necesarios para absorber todas las demandas del mercado; en especial la organización de congresos, ferias y grandes reuniones de todo tipo.

El edificio posee aparcamiento público, amplias zonas de exposición, dos auditorios con capacidad para 913 y 1.814 personas respectivamente, una sala polivalente de   y treinta salas de distintas capacidades.

Historia
En los años 80 se decide destinar la zona al turismo, los negocios y los congresos. La zona se bautizaría como Campo de las Naciones.

En 1988 la empresa pública municipal gestora que iba a gestionar este inmueble fue bautizada como Campo de las Naciones. En 2006, cuando ya gestionaba este Palacio y otros edificios expositivos de la ciudad, cambió su nombre por Madrid Espacios y Congresos S.A., con el nombre comercial de Madridec. No obstante, Madridec quebró en 2013 y el propio Ayuntamiento pasó a ser gestor de sus deudas y activos.

En 1991 se inauguran cerca de él las instalaciones feriales del Ifema.

Entre otros eventos, ha albergado cumbres internacionales de la OTAN, de la Unión Europea y de la Asamblea Anual del Fondo Monetario Internacional.

El 15 de diciembre de 1995 se acordó en su interior denominar euro a la moneda única europea.

En octubre de 2003 se celebró la Conferencia Internacional de Donantes para la Reconstrucción de Irak.

Desde enero de 2019 es gestionado por IFEMA.

Véase también
Palacio de Congresos (Paseo de la Castellana)

Referencias

Enlaces externos

Presentación del Palacio Municipal de Congresos en la Web de la empresa municipal Madrid Espacios y Congresos

Obras de Ricardo Bofill
Municipal de Congresos de Madrid
Congresos
Barajas
Arquitectura cultural de España del siglo XX
Arquitectura en la Comunidad de Madrid en el siglo XX
Arquitectura de España de 1993

redpajama_set_name: RedPajamaWikipedia


Founded in 1997 by legendary Montreal indie-rockers, GrimSkunk, when they suddenly found themselves without a distributor, Indica Records was started with $2000; a grassroots, do-it-yourself approach; and an overriding belief in "artist first" vision.
Armed with the mandate of supporting independent releases for great touring bands, lacking label support in Canada, Indica has since grown by leaps and bounds and has expanded its repertoire to encompass a variety of genres beyond the rock/punk/alternative music on which the label was founded, including folk, pop, indie, world and electro/rock.
The company's vision has not waivered, however. ³We find artists with a great attitude, songwriting and originality. What we put out has to be special, genuine, and capable of moving people. Our vision is simple: we are fans first."
You can submit us your demos by email via demo@indica.mu.


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A mathematical dynamic model is derived for the compliant constant-force mechanism, based on the pseudo-rigid-body model simplification of the device. The compliant constant-force mechanism is a slider mechanism incorporating large-deflection beams, which outputs near-constant-force across the range of its designed deflection. The equation of motion is successfully validated with empirical data from five separate mechanisms, comprising two configurations of compliant constant-force mechanism. The dynamic model is cast in generalized form to represent all possible configurations of compliant constant-force mechanism. Deriving the dynamic equation from the pseudo-rigid-body model is useful because every configuration is represented by the same model, so a separate treatment is not required for each configuration. An unexpected dynamic trait of the constant-force mechanism is discovered: there exists a range of frequencies for which the output force of the mechanism accords nearer to constant-force than does the output force at static levels.


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About ML2R
Leadership and organization
Associated Research Groups
Coordination AI Network
Trustworthy ML
Hybrid ML
Resource-Aware ML
Quantum ML
Learn more about our Research
The ML2R scientists conduct research on Machine Learning (ML) solutions and thus the technological foundations for Artificial Intelligence. We view ML as a service of the future that is oriented toward people and earns the trust of users. Our solutions, based on both data and knowledge, deliver reliable and verifiable results. We design ML applications that meet the highest quality standards, preserve resources, and function on a wide variety of platforms, from mobile devices to quantum computers.
Trustworthy Machine Learning
Robust and verifiable ML processes strengthen trust in the technology.
To the Research Area
The combination of data and knowledge opens up new fields of application.
Resource-Aware Machine Learning
Resource-conscious ML saves energy, storage space and computing capacity.
Machine Learning on Quantum Computers
Quantum computers transcend the limitations of classical computers.
Dr. Sebastian Houben
Trustworthy Machine Learning aims to utilize robust and verifiable methods to increase trust in ML and AI applications and allow for the certification of these technologies.
Machine Learning (ML) and Artificial Intelligence (AI) applications are increasingly becoming an integral part of our everyday lives. In the process, the technologies are also affecting areas that directly impact people's lives and health, such as medicine, autonomous driving, or human resources management. Especially in these fields of application, it is necessary that decisions are fair and free of any bias, that the procedures are robust, and that the results are verifiable.
What is Trustworthy Machine Learning?
With their research on trustworthy Machine Learning, ML2R scientists aim at strengthening trust in ML and AI applications. The basis of this work lies in the development of robust and verifiable ML methods. In addition, the researchers are working on the design of modular and hybrid ML applications that enable the application of reusable, standardized ML components.
To this end, researchers are taking the following approaches:
Ensuring explainability of AI and ML applications by making operating principles and results interpretable for human experts.
Increasing the transparency of technologies to make them safe and reliable for end users who do not have scientific expertise.
Enabling AI safety and certification by testing and evaluating the entire AI process – from the selection of training data to its use in practice.
Measuring Trustworthiness of an AI Application
An additional approach is the development of evaluation and quality criteria for AI. Criteria for compliance with ethical standards, data protection and privacy hereby play a central role. Mathematical methods and proofs are used when making quantitative statements about quality, specifying quality criteria for ML methods and describing the accuracy of the results.
Supporting End Users
The researchers at ML2R also have those users in mind who would like to use AI without having to delve deeply into the mathematical basics of ML. To this end, ML2R scientists are working on forms of representation that contain the essential information of a Machine Learning method in a compact and comprehensible way, similar to the package insert of a drug or the care instructions for an item of clothing: What application scenarios is the AI intended for? What requirements are necessary and how accurate are the expected results?
More about research & publications
How can autonomous driving become safer? As part of the BMWi-funded project "AI Safety", ML2R scientists are participating in research on the safety and trustworthiness of AI systems. The researchers have published the white paper "Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety". Using autonomous driving among others as a case example, the white paper presents challenges and solutions for the safe application of AI.
To the Blog Post
Dr. Ramses Sanchez
Hybrid Machine Learning
Hybrid ML integrates knowledge from multiple sources into learning systems to produce reliable results even in regard to small or uncertain stocks of data.
ML2R researchers are working on methods to make heterogeneous data and complex knowledge usable for Machine Learning. To this end, they are investigating methods to process knowledge in a uniform manner and to uncover structures in data. In addition, data-based forms of ML, such as Deep Learning using Artificial Neural Networks, are linked with other forms of learning that are grounded in explicit and logical knowledge.
What is hybrid Machine Learning?
Hybrid ML aims to address both practical and fundamental problems of Machine Learning and Artificial Intelligence. The hybrid approach hereby pertains to the application of both classical, data-based ML methods and knowledge-based methods. ML applications that consist of flexibly deployable, reusable modules offer great opportunities to combine different methods. In developing hybrid ML solutions, researchers not only draw on concepts from computer science, but also on knowledge and methods from other disciplines, such as mathematics, statistical physics, linguistics, and psychology.
What is the Research Focus?
Research on hybrid ML can be divided into three main sections:
Informed Machine Learning imposes data-independent domain knowledge into training algorithms or model classes to improve training time and generalization concerning real-life applications.
Representation Learning aims to uncover and explain semantically meaningful and causal factors of variations in data. This makes it possible to supplement or replace missing or insufficient training data with the help of machine-generated data.
Theoretical Machine Learning is concerned with the fundamental investigation of ML algorithms and learning procedures. It thus provides the theoretical basis for statements on interpretability, precision of results and validity ranges.
In Practice: Logistics
ML methods often work particularly well when they can learn with a lot of data. This differs greatly from situations where only few or even no data are available. In logistics, for example, weather influences, construction measures or accidents can cause exceptional situations for which no sufficiently large amounts of data exist.
This example also illustrates another challenge of ML: Data and knowledge stem from different sources and present diverging formats. Containers used to transport goods are increasingly equipped with sensors that transmit information about their current position as well as the pressure and temperature inside. This sensor data must then be linked with knowledge from other information sources, such as traffic planning systems and weather data. Sometimes knowledge is even only available in the form of experience or intuition of human experts. For applications in logistics, systems therefore have to be developed in a way that makes complex relationships in logistics planning transparent to humans and at the same time makes optimal use of the potential of ML.
Informed Machine Learning – Learning from Data and Prior Knowledge
In her Blog post, ML2R scientist Laura von Rueden explains the basics of Informed Machine Learning. The researcher describes how already existing knowledge sources can be utilized for Machine Learning. The possibilities for integrating knowledge are clearly illustrated step by step and the advantages are highlighted. Informed ML makes it possible to train models even with small amounts of data and to validate already trained models with the help of prior knowledge.
Dr. Helena Kotthaus
Resource-aware ML enables computations to be performed reliably using Machine Learning, even on small devices such as smartphones or directly within sensors.
The developers at ML2R are working on making Machine Learning available even on devices with restricted computing power and limited energy and memory resources. For safety-critical applications in particular, ML methods must be designed in such a way that data can be reliably processed within a given time frame. In addition, the researchers are optimizing ML applications to require as little energy and computing resources as possible.
What is Resource-Aware Machine Learning?
Resource-aware ML provides methods that minimize the consumption of energy, storage space, and computing capacity. These resource-aware ML methods are able to process large amounts of data efficiently and quickly without compromising on the quality of the results. With the growing heterogeneity of modern computer architectures, it is also becoming increasingly important that ML procedures are adapted to specific execution platforms and optimized for them.
In which Fields of Application is Resource Efficiency particularly in Demand?
In addition to the actual production, modern, automated production plants generate large amounts of data. Examples are the operating data from machines or measurement results from sensors. The evaluation of this large stock of data holds enormous potential for optimization: Processes can be accelerated, quality improved, and machines can be maintained with foresight. In the past, powerful computing centers were often used for such Big Data applications.
However, the increasing linkage of physical and virtual worlds in the "Internet of Things" (IoT) requires decentralized data processing. Particularly for applications in Industry 4.0 and logistics, it is not always possible – for example, due to a lack of bandwidth for data transmission – to send all data to a central computer. Instead, the devices that record the data, such as sensors, must already conduct parts of the processing and analysis.
Limiting the Consumption of Energy, Storage and Computing Capacity
A first step in the development of resource-efficient ML methods is the analysis of resource requirements. The properties of the algorithms used must be considered as well as the specific implementations on different computer architectures.
A further approach is to develop hardware and software optimized for specific learning tasks. FPGAs (Field Programmable Gate Arrays), which can be used as energy-efficient, flexibly configurable circuits in various application scenarios, are particularly suitable for IoT applications.
Finally, one research approach is to simplify ML algorithms so that they can be executed with less memory and computing capacity. This often involves the use of approximation methods, which must be theoretically well-founded and provided with the specification of error bounds.
Summer School on Resource-Aware Machine Learning
The international Summer School "Resource-aware Machine Learning" was well-received within the international research community, attracting over 830 registered participants from 64 countries. The event focused on Machine Learning and data analysis with limited resources. In addition to a hackathon on a current task from the field of logistics, the program included lectures, interactive formats and opportunities to meet and network. The online event was jointly organized by the Competence Center ML2R and the Collaborative Research Center 876 "Providing Information by Resource-Constrained Data Analysis", the latter of which is part of the Faculty of Computer Science at TU Dortmund University.
More information on the Summer School
Dr. Nico Piatkowski
Quantum computers can compute a variety of solution paths in parallel and thus have the potential to process information faster and handle more complex tasks than classical digital computers.
Classical computers reach their limits time and again. This is particularly evident in applications from the fields of Big Data and Artificial Intelligence, when it is a matter of processing large amounts of data, calculating many different solution paths or selecting the optimal result from a great number of options. Today, such tasks can often only be accomplished with a tremendous amount of time and computing effort, and some are even practically unsolvable because the calculations would take years.
How does Machine Learning work on Quantum Computers?
Quantum computers have the potential to overcome the fundamental limitations of classical computers. Today, quantum computing is more than just a theoretical concept. After considerable investments in research and development, the first technical implementations of quantum computers exist, which will also be available to researchers at ML2R in the future.
Using Quantum Effects for Faster Calculations
Quantum computers use quantum effects such as superposition or entanglement for information processing and can thus, in principle, deliver results faster. While a digital computer computes with bits, a quantum computer works with qubits, which, unlike classical bits, can assume not only one of two possible states, but also any superposition of the two.
What are Current Research Questions?
Research projects at ML2R address the question of how these quantum effects can best be used for Machine Learning. It has already been shown that Machine Learning methods can be adapted for quantum computers in such a way that multiple solution paths are followed simultaneously. Some quantum algorithm-based methods are significantly faster than classical computations. Even a single quantum computer can thus find solutions faster than several classical computers operating in a cluster.
Quantum Machine Learning – An Analysis on Competence, Research and Application
Quantum computers will profoundly change Artificial Intelligence and Machine Learning and open up new fields and possibilities of application. The study on Machine Learning on Quantum Computers, developed with the participation of researchers from ML2R, introduces basic concepts and technologies of quantum computing, analyzes the current state of research and competence dispersion, and identifies market potentials.
The study "Quantum Machine Learning" gives an insight into quantum computing, explains which physical effects play a role and how they are used to accelerate Machine Learning methods. In addition to the logical components, techniques for implementing the hardware of quantum computers are presented. The study also provides an overview of the current state of research and competence dispersion and ranks Germany's position in international competition. In addition, the study presents concrete application areas and market potentials for various industries.
Request the Study (free of charge)
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