Hybrid Machine Translation combines the benefits of both translation memory and customized machine translation. In this work, we present a case study with British Library on the benefit of Hybrid MT.
This document discusses different types of machine translation, including statistical machine translation (SMT), rule-based machine translation (RBMT), and hybrid machine translation. It provides details on the SMT training and decoding processes, considerations for SMT and RBMT, common machine translation applications like Google Translate, Microsoft Translator, and SDL Language Weaver, and the types of files and content that can be machine translated.
MT best practices for price, speed AND quality, as well as Lexcelera’s machine translation case studies and services including training, integration, post-editing and hosted MT
A Moses engine for legal translation
This presentation is a part of the MosesCore project that encourages the development and usage of open source machine translation tools, notably the Moses statistical MT toolkit.
MosesCore is supporetd by the European Commission Grant Number 288487 under the 7th Framework Programme.
Latest news on Twitter - #MosesCore
Dr Mark Hooper's presentation from our April Translation and Transcreation Workshop. How does the translation process work? How can you get the best for your translation project from your Language Service Provider? What does technology mean for translation? What are the differences between translation, transcreation and localisation? This informative presentation answers all of these questions and more.
Panelists: Yoshiyasu Yamakawa (Intel), JP Barraza (Systran), Konstantin Dranch (Memsource), David Koot (TAUS)
The focus of this session will be on predictions and risk management. What kind of things can you predict and how can you manage risks by by analyzing your translation data or monitoring your productivity and quality. Tracking translation data in different cycles of the translation process (translation, post-editing, review, proof-reading) offers tremendous value when it comes to predicting future trends or making informed choices. What type of data can be valuable and what kind of predictions can we make using this data? How can we make more efficient use of already available data? How can we use this type of data to improve machine translation, automatic QA, error-recognition, sampling or quality estimation? How can academia and industry work together towards a common goal?
This document discusses how to build your own statistical machine translation engines. It provides an overview of statistical machine translation compared to rule-based machine translation. It outlines the requirements, preparation steps, and DoMY (Do-Moses-Yourself) toolkit for building an SMT system. Key steps include collecting parallel corpora, preprocessing the data, training language and translation models, and evaluating the engine. Regular retraining and feedback can help improve the system over time.
This document discusses different types of machine translation, including statistical machine translation (SMT), rule-based machine translation (RBMT), and hybrid machine translation. It provides details on the SMT training and decoding processes, considerations for SMT and RBMT, common machine translation applications like Google Translate, Microsoft Translator, and SDL Language Weaver, and the types of files and content that can be machine translated.
MT best practices for price, speed AND quality, as well as Lexcelera’s machine translation case studies and services including training, integration, post-editing and hosted MT
A Moses engine for legal translation
This presentation is a part of the MosesCore project that encourages the development and usage of open source machine translation tools, notably the Moses statistical MT toolkit.
MosesCore is supporetd by the European Commission Grant Number 288487 under the 7th Framework Programme.
Latest news on Twitter - #MosesCore
Dr Mark Hooper's presentation from our April Translation and Transcreation Workshop. How does the translation process work? How can you get the best for your translation project from your Language Service Provider? What does technology mean for translation? What are the differences between translation, transcreation and localisation? This informative presentation answers all of these questions and more.
Panelists: Yoshiyasu Yamakawa (Intel), JP Barraza (Systran), Konstantin Dranch (Memsource), David Koot (TAUS)
The focus of this session will be on predictions and risk management. What kind of things can you predict and how can you manage risks by by analyzing your translation data or monitoring your productivity and quality. Tracking translation data in different cycles of the translation process (translation, post-editing, review, proof-reading) offers tremendous value when it comes to predicting future trends or making informed choices. What type of data can be valuable and what kind of predictions can we make using this data? How can we make more efficient use of already available data? How can we use this type of data to improve machine translation, automatic QA, error-recognition, sampling or quality estimation? How can academia and industry work together towards a common goal?
This document discusses how to build your own statistical machine translation engines. It provides an overview of statistical machine translation compared to rule-based machine translation. It outlines the requirements, preparation steps, and DoMY (Do-Moses-Yourself) toolkit for building an SMT system. Key steps include collecting parallel corpora, preprocessing the data, training language and translation models, and evaluating the engine. Regular retraining and feedback can help improve the system over time.
Cloud-based translation automation is quickly becoming a trend. This changes the whole working routine of professional translators. This presentation unveils key challenges and aspirations of professional translators based on the latest research conducted by ABBYY Language services in April 2014. It shows how and when one can utilize the opportunities for collaborative translation provided by cloud technologies.
COLT, the COntinuously Learning Translation SystemAdamWooten
COLT combines the best of human translation and machine translation, to give you predictable budget, dramatically-reduced cost, and maximum quality where needed.
The document discusses the evolution of machine translation technology from early systems in the 1950s to modern statistical machine translation approaches. It outlines key developments like increased processing power, large datasets, and integration with other tools. While making progress, current statistical MT still has limitations in areas like syntax, morphology, and context. The document suggests future systems may need a new approach, discussing neuroscience concepts like hierarchical temporal memory as a way to better simulate human intelligence.
The document discusses the evolution of machine translation technology from early systems in the 1950s to modern statistical machine translation approaches. It outlines key developments like increased processing power, large aligned text corpora, and integration with other language technologies. While making progress, current statistical MT still has limitations in areas like morphology, syntax and ambiguity. The document proposes that future advances may require new approaches inspired by neuroscience understanding of human intelligence and the brain's hierarchical temporal memory architecture.
This presentation is a part of the MosesCore project that encourages the development and usage of open source machine translation tools, notably the Moses statistical MT toolkit. MosesCore is supported by the European Commission Grant Number 288487 under the 7th Framework Programme.
For the latest updates go to http://www.statmt.org/mosescore/
or follow us on Twitter - #MosesCore
ESR2 Santanu Pal - EXPERT Summer School - Malaga 2015RIILP
The document describes a statistical automatic post-editing (APE) system that aims to improve machine translation output with minimal human effort. The system uses hierarchical phrase-based statistical machine translation trained on machine translation output and reference human translations. The system first cleans and preprocesses data, generates improved word alignments, and then performs hierarchical phrase-based SMT to output post-edits. Evaluation shows the APE system outperforms the baseline machine translation according to both automatic metrics and human evaluation, requiring less post-editing effort.
Three engineers, at various points, each take their own approach adding Rust to a C codebase, each being more and more ambitious. I initially just wanted to replace the server’s networking and event loop with an equally fast Rust implementation. We’d reuse many core components that were in C and just call into them from Rust. Surely it wouldn’t be that much code…
Pelikan is Twitter’s open source and modular framework for in-memory caching, allowing us to replace Memcached and Redis forks with a single codebase and achieve better performance. At Twitter, we operate hundreds of cache clusters storing hundreds of terabytes of small objects in memory. In-memory caching is critical, and demands performance, reliability, and efficiency.
In this talk, I’ll share my adventures in working on Pelikan and how rewriting it in Rust can be more than just a meme.
Translation memory (TM) software stores previous source and target language translations to retrieve and reuse for future translations, saving time and money. TM software like Trados matches new source text to stored translations, presenting exact or similar matches to translators. Using TM can increase translator productivity by 10-50% and reduce costs for repetitive documents by only requiring them to be translated once.
Is Your Enterprise “fire-fighting” translation issues? Optimize the process w...dclsocialmedia
Join Scott Carothers, Senior Globalization Executive at Kinetic the Technology Agency for an overview of specific translation metrics that will assist your enterprise in optimizing the translation process, and assist you in leading your organization as an advocate of continual process improvement.
Experiments with Different Models of Statistcial Machine Translationkhyati gupta
We have chosen Statistical machine translation approach for our thesis. Statistical machine translation work on parallel data. We performed our thesis on Hindi-English language pair. SMT uses different models for performing translation.
Experiments with Different Models of Statistcial Machine Translationkhyati gupta
This document summarizes an experiment conducted on statistical machine translation models. The experiment compared phrase-based, hierarchical, and syntax-based statistical machine translation models. The document outlines the process of data preparation including tokenization, alignment, and training on the Moses platform. It then describes how each model - phrase-based, hierarchical, and syntax-based - works, including rule extraction for the hierarchical model. The document concludes by discussing the advantage of the hierarchical model and how it was able to automatically annotate Hindi data.
The document summarizes work done on experimenting with different models of statistical machine translation (SMT). It discusses various SMT models studied including phrase-based, hierarchical, syntax-based, and hybrid translation models. The document outlines the process of data preparation, training, tuning and evaluation of models on a Hindi-English language pair. Results showed that the hierarchical and syntax-based models performed better than phrase-based in terms of reordering words and producing grammatically correct sentences for the given language pair.
SDL is the leader in global content management and language translation solutions. With more than 20 years of experience, SDL helps companies build relevant online experiences that deliver transformative business results on a global scale. Translation Industry continues to grow, and Freelancers, LSPs and Corporate clients all see increased demand as more and more content is created, so we have to address them all. As a Market-leading translation productivity tool, SDL Trados Studio is trusted by over 200,000 translation professionals to boost productivity, control quality and aid collaboration. SDL has launched Trados Studio 2017. This presentation will introduce SDL Trados Studio 2017 and highlight SDL’s new productivity booster- UPLIFT, which is well welcomed by global clients.
Building a Neural Machine Translation System From ScratchNatasha Latysheva
Human languages are complex, diverse and riddled with exceptions – translating between different languages is therefore a highly challenging technical problem. Deep learning approaches have proved powerful in modelling the intricacies of language, and have surpassed all statistics-based methods for automated translation. This session begins with an introduction to the problem of machine translation and discusses the two dominant neural architectures for solving it – recurrent neural networks and transformers. A practical overview of the workflow involved in training, optimising and adapting a competitive neural machine translation system is provided. Attendees will gain an understanding of the internal workings and capabilities of state-of-the-art systems for automatic translation, as well as an appreciation of the key challenges and open problems in the field.
240115_Attention Is All You Need (2017 NIPS).pptxthanhdowork
Min-Seo Kim works at the Network Science Lab at the Catholic University of Korea. The document discusses previous work on recurrent neural networks (RNNs), long short-term memory (LSTMs), and gated recurrent units (GRUs) for processing sequential data. It then introduces the Transformer, which uses self-attention rather than recurrent layers, and applies it to machine translation tasks with better performance than other models. Experiments show the Transformer achieves higher accuracy than other architectures on an English-to-German translation task and demonstrates good performance on English constituency parsing despite not being specifically tuned for that task.
1) MTPE improves translation efficiency and quality while allowing for more scalable management compared to traditional translation.
2) Vertical MT training is important to build confidence in results by focusing on specific domains.
3) Terminology plays a key role by being extracted from documents, applied to MT, and collected for future use.
This webinar provides an overview of the XTM translation management system. It discusses the translation process and requirements of project managers, clients, and linguists. XTM is presented as a complete translation ecosystem that can help collaborators work more efficiently through real-time data sharing and tracking. The webinar agenda includes a demonstration of XTM's features and a question and answer session.
Language Quality Management: Models, Measures, Methodologies Sajan
With growing content, shorter release dates and many target languages, it’s important for global companies to have a process in place to track and measure translation effectiveness. Learn how big companies like Microsoft, Snap-on Diagnostics and Symantec manage translation quality.
Desired language characteristics – Data typing .pptx4132lenin6497ram
This document discusses various topics related to programming languages and tools for real-time systems. It explores desired language characteristics for real-time systems like readability, modularity, and support for meeting deadlines. It also covers data typing, control structures, hierarchical decomposition using blocks, procedures and functions. Additional topics include packages, error handling, multitasking, low-level programming, scheduling and experimental languages. Examples of languages used for real-time systems are given like C, C++, Ada, Assembly and Python.
The document discusses two research topics:
1. A web transcoding framework that aims to automatically and transparently convert web pages for viewing on mobile devices while maintaining quality. Current solutions require manual work or result in unusable pages.
2. A performance estimation framework for heterogeneous multiprocessor system-on-chip platforms. It proposes using timed transaction level models for fast and accurate early performance estimation of candidate application mappings, as current techniques are too slow. The document outlines the proposed design methodology and provides example results estimating an MP3 decoder on an FPGA board.
Evidence of Jet Activity from the Secondary Black Hole in the OJ 287 Binary S...Sérgio Sacani
Wereport the study of a huge optical intraday flare on 2021 November 12 at 2 a.m. UT in the blazar OJ287. In the binary black hole model, it is associated with an impact of the secondary black hole on the accretion disk of the primary. Our multifrequency observing campaign was set up to search for such a signature of the impact based on a prediction made 8 yr earlier. The first I-band results of the flare have already been reported by Kishore et al. (2024). Here we combine these data with our monitoring in the R-band. There is a big change in the R–I spectral index by 1.0 ±0.1 between the normal background and the flare, suggesting a new component of radiation. The polarization variation during the rise of the flare suggests the same. The limits on the source size place it most reasonably in the jet of the secondary BH. We then ask why we have not seen this phenomenon before. We show that OJ287 was never before observed with sufficient sensitivity on the night when the flare should have happened according to the binary model. We also study the probability that this flare is just an oversized example of intraday variability using the Krakow data set of intense monitoring between 2015 and 2023. We find that the occurrence of a flare of this size and rapidity is unlikely. In machine-readable Tables 1 and 2, we give the full orbit-linked historical light curve of OJ287 as well as the dense monitoring sample of Krakow.
More Related Content
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Cloud-based translation automation is quickly becoming a trend. This changes the whole working routine of professional translators. This presentation unveils key challenges and aspirations of professional translators based on the latest research conducted by ABBYY Language services in April 2014. It shows how and when one can utilize the opportunities for collaborative translation provided by cloud technologies.
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This presentation is a part of the MosesCore project that encourages the development and usage of open source machine translation tools, notably the Moses statistical MT toolkit. MosesCore is supported by the European Commission Grant Number 288487 under the 7th Framework Programme.
For the latest updates go to http://www.statmt.org/mosescore/
or follow us on Twitter - #MosesCore
ESR2 Santanu Pal - EXPERT Summer School - Malaga 2015RIILP
The document describes a statistical automatic post-editing (APE) system that aims to improve machine translation output with minimal human effort. The system uses hierarchical phrase-based statistical machine translation trained on machine translation output and reference human translations. The system first cleans and preprocesses data, generates improved word alignments, and then performs hierarchical phrase-based SMT to output post-edits. Evaluation shows the APE system outperforms the baseline machine translation according to both automatic metrics and human evaluation, requiring less post-editing effort.
Three engineers, at various points, each take their own approach adding Rust to a C codebase, each being more and more ambitious. I initially just wanted to replace the server’s networking and event loop with an equally fast Rust implementation. We’d reuse many core components that were in C and just call into them from Rust. Surely it wouldn’t be that much code…
Pelikan is Twitter’s open source and modular framework for in-memory caching, allowing us to replace Memcached and Redis forks with a single codebase and achieve better performance. At Twitter, we operate hundreds of cache clusters storing hundreds of terabytes of small objects in memory. In-memory caching is critical, and demands performance, reliability, and efficiency.
In this talk, I’ll share my adventures in working on Pelikan and how rewriting it in Rust can be more than just a meme.
Translation memory (TM) software stores previous source and target language translations to retrieve and reuse for future translations, saving time and money. TM software like Trados matches new source text to stored translations, presenting exact or similar matches to translators. Using TM can increase translator productivity by 10-50% and reduce costs for repetitive documents by only requiring them to be translated once.
Is Your Enterprise “fire-fighting” translation issues? Optimize the process w...dclsocialmedia
Join Scott Carothers, Senior Globalization Executive at Kinetic the Technology Agency for an overview of specific translation metrics that will assist your enterprise in optimizing the translation process, and assist you in leading your organization as an advocate of continual process improvement.
Experiments with Different Models of Statistcial Machine Translationkhyati gupta
We have chosen Statistical machine translation approach for our thesis. Statistical machine translation work on parallel data. We performed our thesis on Hindi-English language pair. SMT uses different models for performing translation.
Experiments with Different Models of Statistcial Machine Translationkhyati gupta
This document summarizes an experiment conducted on statistical machine translation models. The experiment compared phrase-based, hierarchical, and syntax-based statistical machine translation models. The document outlines the process of data preparation including tokenization, alignment, and training on the Moses platform. It then describes how each model - phrase-based, hierarchical, and syntax-based - works, including rule extraction for the hierarchical model. The document concludes by discussing the advantage of the hierarchical model and how it was able to automatically annotate Hindi data.
The document summarizes work done on experimenting with different models of statistical machine translation (SMT). It discusses various SMT models studied including phrase-based, hierarchical, syntax-based, and hybrid translation models. The document outlines the process of data preparation, training, tuning and evaluation of models on a Hindi-English language pair. Results showed that the hierarchical and syntax-based models performed better than phrase-based in terms of reordering words and producing grammatically correct sentences for the given language pair.
SDL is the leader in global content management and language translation solutions. With more than 20 years of experience, SDL helps companies build relevant online experiences that deliver transformative business results on a global scale. Translation Industry continues to grow, and Freelancers, LSPs and Corporate clients all see increased demand as more and more content is created, so we have to address them all. As a Market-leading translation productivity tool, SDL Trados Studio is trusted by over 200,000 translation professionals to boost productivity, control quality and aid collaboration. SDL has launched Trados Studio 2017. This presentation will introduce SDL Trados Studio 2017 and highlight SDL’s new productivity booster- UPLIFT, which is well welcomed by global clients.
Building a Neural Machine Translation System From ScratchNatasha Latysheva
Human languages are complex, diverse and riddled with exceptions – translating between different languages is therefore a highly challenging technical problem. Deep learning approaches have proved powerful in modelling the intricacies of language, and have surpassed all statistics-based methods for automated translation. This session begins with an introduction to the problem of machine translation and discusses the two dominant neural architectures for solving it – recurrent neural networks and transformers. A practical overview of the workflow involved in training, optimising and adapting a competitive neural machine translation system is provided. Attendees will gain an understanding of the internal workings and capabilities of state-of-the-art systems for automatic translation, as well as an appreciation of the key challenges and open problems in the field.
240115_Attention Is All You Need (2017 NIPS).pptxthanhdowork
Min-Seo Kim works at the Network Science Lab at the Catholic University of Korea. The document discusses previous work on recurrent neural networks (RNNs), long short-term memory (LSTMs), and gated recurrent units (GRUs) for processing sequential data. It then introduces the Transformer, which uses self-attention rather than recurrent layers, and applies it to machine translation tasks with better performance than other models. Experiments show the Transformer achieves higher accuracy than other architectures on an English-to-German translation task and demonstrates good performance on English constituency parsing despite not being specifically tuned for that task.
1) MTPE improves translation efficiency and quality while allowing for more scalable management compared to traditional translation.
2) Vertical MT training is important to build confidence in results by focusing on specific domains.
3) Terminology plays a key role by being extracted from documents, applied to MT, and collected for future use.
This webinar provides an overview of the XTM translation management system. It discusses the translation process and requirements of project managers, clients, and linguists. XTM is presented as a complete translation ecosystem that can help collaborators work more efficiently through real-time data sharing and tracking. The webinar agenda includes a demonstration of XTM's features and a question and answer session.
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The document discusses two research topics:
1. A web transcoding framework that aims to automatically and transparently convert web pages for viewing on mobile devices while maintaining quality. Current solutions require manual work or result in unusable pages.
2. A performance estimation framework for heterogeneous multiprocessor system-on-chip platforms. It proposes using timed transaction level models for fast and accurate early performance estimation of candidate application mappings, as current techniques are too slow. The document outlines the proposed design methodology and provides example results estimating an MP3 decoder on an FPGA board.
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Wereport the study of a huge optical intraday flare on 2021 November 12 at 2 a.m. UT in the blazar OJ287. In the binary black hole model, it is associated with an impact of the secondary black hole on the accretion disk of the primary. Our multifrequency observing campaign was set up to search for such a signature of the impact based on a prediction made 8 yr earlier. The first I-band results of the flare have already been reported by Kishore et al. (2024). Here we combine these data with our monitoring in the R-band. There is a big change in the R–I spectral index by 1.0 ±0.1 between the normal background and the flare, suggesting a new component of radiation. The polarization variation during the rise of the flare suggests the same. The limits on the source size place it most reasonably in the jet of the secondary BH. We then ask why we have not seen this phenomenon before. We show that OJ287 was never before observed with sufficient sensitivity on the night when the flare should have happened according to the binary model. We also study the probability that this flare is just an oversized example of intraday variability using the Krakow data set of intense monitoring between 2015 and 2023. We find that the occurrence of a flare of this size and rapidity is unlikely. In machine-readable Tables 1 and 2, we give the full orbit-linked historical light curve of OJ287 as well as the dense monitoring sample of Krakow.
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We present the JWST discovery of SN 2023adsy, a transient object located in a host galaxy JADES-GS
+
53.13485
−
27.82088
with a host spectroscopic redshift of
2.903
±
0.007
. The transient was identified in deep James Webb Space Telescope (JWST)/NIRCam imaging from the JWST Advanced Deep Extragalactic Survey (JADES) program. Photometric and spectroscopic followup with NIRCam and NIRSpec, respectively, confirm the redshift and yield UV-NIR light-curve, NIR color, and spectroscopic information all consistent with a Type Ia classification. Despite its classification as a likely SN Ia, SN 2023adsy is both fairly red (
�
(
�
−
�
)
∼
0.9
) despite a host galaxy with low-extinction and has a high Ca II velocity (
19
,
000
±
2
,
000
km/s) compared to the general population of SNe Ia. While these characteristics are consistent with some Ca-rich SNe Ia, particularly SN 2016hnk, SN 2023adsy is intrinsically brighter than the low-
�
Ca-rich population. Although such an object is too red for any low-
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cosmological sample, we apply a fiducial standardization approach to SN 2023adsy and find that the SN 2023adsy luminosity distance measurement is in excellent agreement (
≲
1
�
) with
Λ
CDM. Therefore unlike low-
�
Ca-rich SNe Ia, SN 2023adsy is standardizable and gives no indication that SN Ia standardized luminosities change significantly with redshift. A larger sample of distant SNe Ia is required to determine if SN Ia population characteristics at high-
�
truly diverge from their low-
�
counterparts, and to confirm that standardized luminosities nevertheless remain constant with redshift.
This presentation offers a general idea of the structure of seed, seed production, management of seeds and its allied technologies. It also offers the concept of gene erosion and the practices used to control it. Nursery and gardening have been widely explored along with their importance in the related domain.
BIRDS DIVERSITY OF SOOTEA BISWANATH ASSAM.ppt.pptxgoluk9330
Ahota Beel, nestled in Sootea Biswanath Assam , is celebrated for its extraordinary diversity of bird species. This wetland sanctuary supports a myriad of avian residents and migrants alike. Visitors can admire the elegant flights of migratory species such as the Northern Pintail and Eurasian Wigeon, alongside resident birds including the Asian Openbill and Pheasant-tailed Jacana. With its tranquil scenery and varied habitats, Ahota Beel offers a perfect haven for birdwatchers to appreciate and study the vibrant birdlife that thrives in this natural refuge.
Anti-Universe And Emergent Gravity and the Dark UniverseSérgio Sacani
Recent theoretical progress indicates that spacetime and gravity emerge together from the entanglement structure of an underlying microscopic theory. These ideas are best understood in Anti-de Sitter space, where they rely on the area law for entanglement entropy. The extension to de Sitter space requires taking into account the entropy and temperature associated with the cosmological horizon. Using insights from string theory, black hole physics and quantum information theory we argue that the positive dark energy leads to a thermal volume law contribution to the entropy that overtakes the area law precisely at the cosmological horizon. Due to the competition between area and volume law entanglement the microscopic de Sitter states do not thermalise at sub-Hubble scales: they exhibit memory effects in the form of an entropy displacement caused by matter. The emergent laws of gravity contain an additional ‘dark’ gravitational force describing the ‘elastic’ response due to the entropy displacement. We derive an estimate of the strength of this extra force in terms of the baryonic mass, Newton’s constant and the Hubble acceleration scale a0 = cH0, and provide evidence for the fact that this additional ‘dark gravity force’ explains the observed phenomena in galaxies and clusters currently attributed to dark matter.
Microbial interaction
Microorganisms interacts with each other and can be physically associated with another organisms in a variety of ways.
One organism can be located on the surface of another organism as an ectobiont or located within another organism as endobiont.
Microbial interaction may be positive such as mutualism, proto-cooperation, commensalism or may be negative such as parasitism, predation or competition
Types of microbial interaction
Positive interaction: mutualism, proto-cooperation, commensalism
Negative interaction: Ammensalism (antagonism), parasitism, predation, competition
I. Mutualism:
It is defined as the relationship in which each organism in interaction gets benefits from association. It is an obligatory relationship in which mutualist and host are metabolically dependent on each other.
Mutualistic relationship is very specific where one member of association cannot be replaced by another species.
Mutualism require close physical contact between interacting organisms.
Relationship of mutualism allows organisms to exist in habitat that could not occupied by either species alone.
Mutualistic relationship between organisms allows them to act as a single organism.
Examples of mutualism:
i. Lichens:
Lichens are excellent example of mutualism.
They are the association of specific fungi and certain genus of algae. In lichen, fungal partner is called mycobiont and algal partner is called
II. Syntrophism:
It is an association in which the growth of one organism either depends on or improved by the substrate provided by another organism.
In syntrophism both organism in association gets benefits.
Compound A
Utilized by population 1
Compound B
Utilized by population 2
Compound C
utilized by both Population 1+2
Products
In this theoretical example of syntrophism, population 1 is able to utilize and metabolize compound A, forming compound B but cannot metabolize beyond compound B without co-operation of population 2. Population 2is unable to utilize compound A but it can metabolize compound B forming compound C. Then both population 1 and 2 are able to carry out metabolic reaction which leads to formation of end product that neither population could produce alone.
Examples of syntrophism:
i. Methanogenic ecosystem in sludge digester
Methane produced by methanogenic bacteria depends upon interspecies hydrogen transfer by other fermentative bacteria.
Anaerobic fermentative bacteria generate CO2 and H2 utilizing carbohydrates which is then utilized by methanogenic bacteria (Methanobacter) to produce methane.
ii. Lactobacillus arobinosus and Enterococcus faecalis:
In the minimal media, Lactobacillus arobinosus and Enterococcus faecalis are able to grow together but not alone.
The synergistic relationship between E. faecalis and L. arobinosus occurs in which E. faecalis require folic acid
Signatures of wave erosion in Titan’s coastsSérgio Sacani
The shorelines of Titan’s hydrocarbon seas trace flooded erosional landforms such as river valleys; however, it isunclear whether coastal erosion has subsequently altered these shorelines. Spacecraft observations and theo-retical models suggest that wind may cause waves to form on Titan’s seas, potentially driving coastal erosion,but the observational evidence of waves is indirect, and the processes affecting shoreline evolution on Titanremain unknown. No widely accepted framework exists for using shoreline morphology to quantitatively dis-cern coastal erosion mechanisms, even on Earth, where the dominant mechanisms are known. We combinelandscape evolution models with measurements of shoreline shape on Earth to characterize how differentcoastal erosion mechanisms affect shoreline morphology. Applying this framework to Titan, we find that theshorelines of Titan’s seas are most consistent with flooded landscapes that subsequently have been eroded bywaves, rather than a uniform erosional process or no coastal erosion, particularly if wave growth saturates atfetch lengths of tens of kilometers.
9. English to Arabic CMT
• Best compe77on grade pipeline involves
– Arabic (de-) tokeniza7on
• Spling morphologically rich words into smaller segments and
vice-versa
• +1.5 BLEU points improvement
– Arabic (de-) normaliza7on
• Mapping different forms of a leaer to one form and vice verse
• +0.5 BLEU point improvement
This ensures high quality but does not guarantee less
frustra7on for post-editors
CMT