For more than a century, the Swiss locomotive, multiple unit, motor coach and railcar classification system, in either its original or updated forms, has been used to name and classify the rolling stock operated on the railways of Switzerland. It started out as a uniform system for the classification and naming of all rolling stock, powered and unpowered, but had been replaced and amended by the UIC classification of goods wagons.
Overview and evolution
The Swiss classification system was created by the Swiss federal railways department, and applied originally to the rolling stock of private railways, operating under government concessions. In 1902, when the Swiss Federal Railways was founded as a government railway, that new railway also became bound by the system.
Unlike the Whyte notation and AAR system, both of which are used to classify wheel arrangements, and the UIC classification of locomotive axle arrangements, the Swiss system, in both its original and updated forms, takes into account a number of other variables, including track gauge, motive power type, and maximum speed. The Swiss system is also less precise than those other systems in the way it deals with axles, because it refers only to numbers, rather than to arrangements, of powered axles, and axles as a whole. The Swiss system is therefore more a method of classifying locomotive and railcar types and series than a method of classifying wheel or axle arrangements.
SwissText - Classification of Large Patent Descriptions
Speakers: Fernando Benites, Dominik Frefel, Joshua Meier and Daniel Perruchoud
Classification of long documents is still a domain for classical machine learning techniques such as TF-IDF or BM25 with Support Vector Machines. Transformers and LSTMs do not scale well with the document length at training and inference time. For patents, this is a critical handicap since the key innovation is often described towards the end of the patent description, which varies in structure and length and can be relatively long.
Furthermore, because the class ontology for patents is very deep, specific classification can only be performed by looking at the differences that might be named in any part of the document. Therefore, it is advantageous to process the whole patent and not only specific parts.
We in...
published: 30 Jun 2022
It's Classification Day For Our Registered Brown Swiss Cows!
We had a judge come in to classify our cows. If you liked it leave a thumbs up, if you have any questions leave a comment, if you thought it was good share it with friends and family, and last but not least if you want to watch it again or see more videos please subscribe.
More videos soon!
Check out Facebook and Instagram @vikingvalleyfarm
published: 09 Jun 2024
An Open Letter to Swiss Miss
First world problems amirite
Intro and outro song:
"Brandenburg Concerto No. 4 in G, Movement I (Allegro), BWV 1049" Kevin MacLeod (incompetech.com)
Licensed under Creative Commons: By Attribution 3.0 License
http://creativecommons.org/licenses/by/3.0/
published: 30 Jun 2016
Brown Swiss classification day! And the red Holstein who can't mind her own ...
As a registered herd, we classify our animals for each lactation. This basically gives the cow a score based on looks. Doesn't matter for making milk but it's important in the show world of dairy cows. We didn't score our better cows this go round. They're yo fresh and still swollen in the udder.
published: 09 Feb 2023
Large Language Model Prompt Chaining for Long Legal Document Classification
Dietrich Trautmann
Prompting is used to guide or steer a language model in generating an appropriate response that is consistent with the desired outcome.
Chaining is a concept that is applied to break down more complex tasks into smaller, manageable tasks.
We use prompt chaining for long legal document classification tasks, since they are difficult to process at once due to the complex domain-specific language as well as the length.
First, we create a concise summary of the original document.
Second, we use semantic search to retrieve related exemplar texts with corresponding annotations from a training corpus.
Finally, we prompt for a label – based on the task – to assign, by leveraging the in-context learning from the few-shot prompt.
We show that we can improve the performance via pro...
published: 27 Sep 2023
Classification Video Group 1 I Swiss Snowsports School Samnaun
Classification Video Group 1 I Swiss Snowsports School Samnaun
I have already completed my first days of skiing.
published: 15 Mar 2019
Classification Video Group 2 I Swiss Snowsports School Samnaun
Classification Video Group 2 I Swiss Snowsports School Samnaun
I can already:
- dash
- brake
- ski single curves
published: 23 Feb 2018
VFH 3D object classification demo with Swiss Ranger 4k and PCL
Proof of concept for indoor 3D object classification using a Swiss Ranger 4k camera and the VFH feature descriptors.
published: 27 Jan 2014
Effleurage
This video demonstrates one of our many different physiotherapy techniques. Effleurage is a massage consisting of rubbing the skin in a circular motion shifting the pain away. Watch as one of our highly trained massage therapists demonstrates.
Book an appointment with one of our massage therapists today by calling us on 0330 088 7800. Or visit our website http://physio.co.uk/ to find out more about our clinics in Manchester and Liverpool.
Follow us on Twitter (@physiocouk), Instagram (physiocouk) and Facebook for more information and news.
published: 06 Feb 2016
Classification Video Group 4 I Swiss Snowsports School Samnaun
Classification Video Group 4 I Swiss Snowsports School Samnaun
I can already:
- controlled plow turns on blue slopes
- control my speed
- ski on blue slopes
Speakers: Fernando Benites, Dominik Frefel, Joshua Meier and Daniel Perruchoud
Classification of long documents is still a domain for classical machine learnin...
Speakers: Fernando Benites, Dominik Frefel, Joshua Meier and Daniel Perruchoud
Classification of long documents is still a domain for classical machine learning techniques such as TF-IDF or BM25 with Support Vector Machines. Transformers and LSTMs do not scale well with the document length at training and inference time. For patents, this is a critical handicap since the key innovation is often described towards the end of the patent description, which varies in structure and length and can be relatively long.
Furthermore, because the class ontology for patents is very deep, specific classification can only be performed by looking at the differences that might be named in any part of the document. Therefore, it is advantageous to process the whole patent and not only specific parts.
We investigate hierarchical approaches that break down documents into smaller parts and other heuristics, such as summarization and hotspot detection, for Bert and PatentBERT and compare them to classical methods. The dataset was downloaded from the European patent office (EPO).
Speakers: Fernando Benites, Dominik Frefel, Joshua Meier and Daniel Perruchoud
Classification of long documents is still a domain for classical machine learning techniques such as TF-IDF or BM25 with Support Vector Machines. Transformers and LSTMs do not scale well with the document length at training and inference time. For patents, this is a critical handicap since the key innovation is often described towards the end of the patent description, which varies in structure and length and can be relatively long.
Furthermore, because the class ontology for patents is very deep, specific classification can only be performed by looking at the differences that might be named in any part of the document. Therefore, it is advantageous to process the whole patent and not only specific parts.
We investigate hierarchical approaches that break down documents into smaller parts and other heuristics, such as summarization and hotspot detection, for Bert and PatentBERT and compare them to classical methods. The dataset was downloaded from the European patent office (EPO).
We had a judge come in to classify our cows. If you liked it leave a thumbs up, if you have any questions leave a comment, if you thought it was good share it w...
We had a judge come in to classify our cows. If you liked it leave a thumbs up, if you have any questions leave a comment, if you thought it was good share it with friends and family, and last but not least if you want to watch it again or see more videos please subscribe.
More videos soon!
Check out Facebook and Instagram @vikingvalleyfarm
We had a judge come in to classify our cows. If you liked it leave a thumbs up, if you have any questions leave a comment, if you thought it was good share it with friends and family, and last but not least if you want to watch it again or see more videos please subscribe.
More videos soon!
Check out Facebook and Instagram @vikingvalleyfarm
First world problems amirite
Intro and outro song:
"Brandenburg Concerto No. 4 in G, Movement I (Allegro), BWV 1049" Kevin MacLeod (incompetech.com)
Licensed ...
First world problems amirite
Intro and outro song:
"Brandenburg Concerto No. 4 in G, Movement I (Allegro), BWV 1049" Kevin MacLeod (incompetech.com)
Licensed under Creative Commons: By Attribution 3.0 License
http://creativecommons.org/licenses/by/3.0/
First world problems amirite
Intro and outro song:
"Brandenburg Concerto No. 4 in G, Movement I (Allegro), BWV 1049" Kevin MacLeod (incompetech.com)
Licensed under Creative Commons: By Attribution 3.0 License
http://creativecommons.org/licenses/by/3.0/
As a registered herd, we classify our animals for each lactation. This basically gives the cow a score based on looks. Doesn't matter for making milk but it's i...
As a registered herd, we classify our animals for each lactation. This basically gives the cow a score based on looks. Doesn't matter for making milk but it's important in the show world of dairy cows. We didn't score our better cows this go round. They're yo fresh and still swollen in the udder.
As a registered herd, we classify our animals for each lactation. This basically gives the cow a score based on looks. Doesn't matter for making milk but it's important in the show world of dairy cows. We didn't score our better cows this go round. They're yo fresh and still swollen in the udder.
Dietrich Trautmann
Prompting is used to guide or steer a language model in generating an appropriate response that is consistent with the desired outcome.
Chai...
Dietrich Trautmann
Prompting is used to guide or steer a language model in generating an appropriate response that is consistent with the desired outcome.
Chaining is a concept that is applied to break down more complex tasks into smaller, manageable tasks.
We use prompt chaining for long legal document classification tasks, since they are difficult to process at once due to the complex domain-specific language as well as the length.
First, we create a concise summary of the original document.
Second, we use semantic search to retrieve related exemplar texts with corresponding annotations from a training corpus.
Finally, we prompt for a label – based on the task – to assign, by leveraging the in-context learning from the few-shot prompt.
We show that we can improve the performance via prompt chaining over zero-shot and even outperforming ChatGPT zero-shot with smaller models in terms of the micro-f1 score.
This presentation was part of SwissText 2023 in Neuchâtel
Dietrich Trautmann
Prompting is used to guide or steer a language model in generating an appropriate response that is consistent with the desired outcome.
Chaining is a concept that is applied to break down more complex tasks into smaller, manageable tasks.
We use prompt chaining for long legal document classification tasks, since they are difficult to process at once due to the complex domain-specific language as well as the length.
First, we create a concise summary of the original document.
Second, we use semantic search to retrieve related exemplar texts with corresponding annotations from a training corpus.
Finally, we prompt for a label – based on the task – to assign, by leveraging the in-context learning from the few-shot prompt.
We show that we can improve the performance via prompt chaining over zero-shot and even outperforming ChatGPT zero-shot with smaller models in terms of the micro-f1 score.
This presentation was part of SwissText 2023 in Neuchâtel
This video demonstrates one of our many different physiotherapy techniques. Effleurage is a massage consisting of rubbing the skin in a circular motion shifting...
This video demonstrates one of our many different physiotherapy techniques. Effleurage is a massage consisting of rubbing the skin in a circular motion shifting the pain away. Watch as one of our highly trained massage therapists demonstrates.
Book an appointment with one of our massage therapists today by calling us on 0330 088 7800. Or visit our website http://physio.co.uk/ to find out more about our clinics in Manchester and Liverpool.
Follow us on Twitter (@physiocouk), Instagram (physiocouk) and Facebook for more information and news.
This video demonstrates one of our many different physiotherapy techniques. Effleurage is a massage consisting of rubbing the skin in a circular motion shifting the pain away. Watch as one of our highly trained massage therapists demonstrates.
Book an appointment with one of our massage therapists today by calling us on 0330 088 7800. Or visit our website http://physio.co.uk/ to find out more about our clinics in Manchester and Liverpool.
Follow us on Twitter (@physiocouk), Instagram (physiocouk) and Facebook for more information and news.
Classification Video Group 4 I Swiss Snowsports School Samnaun
I can already:
- controlled plow turns on blue slopes
- control my speed
- ski on blue slopes
Classification Video Group 4 I Swiss Snowsports School Samnaun
I can already:
- controlled plow turns on blue slopes
- control my speed
- ski on blue slopes
Classification Video Group 4 I Swiss Snowsports School Samnaun
I can already:
- controlled plow turns on blue slopes
- control my speed
- ski on blue slopes
Speakers: Fernando Benites, Dominik Frefel, Joshua Meier and Daniel Perruchoud
Classification of long documents is still a domain for classical machine learning techniques such as TF-IDF or BM25 with Support Vector Machines. Transformers and LSTMs do not scale well with the document length at training and inference time. For patents, this is a critical handicap since the key innovation is often described towards the end of the patent description, which varies in structure and length and can be relatively long.
Furthermore, because the class ontology for patents is very deep, specific classification can only be performed by looking at the differences that might be named in any part of the document. Therefore, it is advantageous to process the whole patent and not only specific parts.
We investigate hierarchical approaches that break down documents into smaller parts and other heuristics, such as summarization and hotspot detection, for Bert and PatentBERT and compare them to classical methods. The dataset was downloaded from the European patent office (EPO).
We had a judge come in to classify our cows. If you liked it leave a thumbs up, if you have any questions leave a comment, if you thought it was good share it with friends and family, and last but not least if you want to watch it again or see more videos please subscribe.
More videos soon!
Check out Facebook and Instagram @vikingvalleyfarm
First world problems amirite
Intro and outro song:
"Brandenburg Concerto No. 4 in G, Movement I (Allegro), BWV 1049" Kevin MacLeod (incompetech.com)
Licensed under Creative Commons: By Attribution 3.0 License
http://creativecommons.org/licenses/by/3.0/
As a registered herd, we classify our animals for each lactation. This basically gives the cow a score based on looks. Doesn't matter for making milk but it's important in the show world of dairy cows. We didn't score our better cows this go round. They're yo fresh and still swollen in the udder.
Dietrich Trautmann
Prompting is used to guide or steer a language model in generating an appropriate response that is consistent with the desired outcome.
Chaining is a concept that is applied to break down more complex tasks into smaller, manageable tasks.
We use prompt chaining for long legal document classification tasks, since they are difficult to process at once due to the complex domain-specific language as well as the length.
First, we create a concise summary of the original document.
Second, we use semantic search to retrieve related exemplar texts with corresponding annotations from a training corpus.
Finally, we prompt for a label – based on the task – to assign, by leveraging the in-context learning from the few-shot prompt.
We show that we can improve the performance via prompt chaining over zero-shot and even outperforming ChatGPT zero-shot with smaller models in terms of the micro-f1 score.
This presentation was part of SwissText 2023 in Neuchâtel
This video demonstrates one of our many different physiotherapy techniques. Effleurage is a massage consisting of rubbing the skin in a circular motion shifting the pain away. Watch as one of our highly trained massage therapists demonstrates.
Book an appointment with one of our massage therapists today by calling us on 0330 088 7800. Or visit our website http://physio.co.uk/ to find out more about our clinics in Manchester and Liverpool.
Follow us on Twitter (@physiocouk), Instagram (physiocouk) and Facebook for more information and news.
Classification Video Group 4 I Swiss Snowsports School Samnaun
I can already:
- controlled plow turns on blue slopes
- control my speed
- ski on blue slopes
For more than a century, the Swiss locomotive, multiple unit, motor coach and railcar classification system, in either its original or updated forms, has been used to name and classify the rolling stock operated on the railways of Switzerland. It started out as a uniform system for the classification and naming of all rolling stock, powered and unpowered, but had been replaced and amended by the UIC classification of goods wagons.
Overview and evolution
The Swiss classification system was created by the Swiss federal railways department, and applied originally to the rolling stock of private railways, operating under government concessions. In 1902, when the Swiss Federal Railways was founded as a government railway, that new railway also became bound by the system.
Unlike the Whyte notation and AAR system, both of which are used to classify wheel arrangements, and the UIC classification of locomotive axle arrangements, the Swiss system, in both its original and updated forms, takes into account a number of other variables, including track gauge, motive power type, and maximum speed. The Swiss system is also less precise than those other systems in the way it deals with axles, because it refers only to numbers, rather than to arrangements, of powered axles, and axles as a whole. The Swiss system is therefore more a method of classifying locomotive and railcar types and series than a method of classifying wheel or axle arrangements.