Presentation from Moscow Data Fest #1, September 12.
Moscow Data Fest is a free one-day event that brings together Data Scientists for sessions on both theory and practice.
Link: http://www.meetup.com/Moscow-Data-Fest/
This document presents research on context-aware neural machine translation. It introduces a model that incorporates context from previous sentences to help with translating ambiguous words like pronouns. The model outperforms baselines on a test set involving pronouns. Analysis shows the model learns to implicitly resolve anaphora by attending to the antecedent noun phrase provided in the context. This suggests the model is capturing this discourse phenomenon without being explicitly trained for anaphora resolution.
The document is an abstracts booklet from the 18th European scientific conference of doctoral students organized by the Faculty of Business and Economics at Mendel University in Brno. It contains abstracts from papers presented at the conference across several sessions, including business economics, economics and finance, and informatics. The abstracts cover topics such as price for ski lift tickets, corporate bankruptcy determinants, machinery investment criteria, organic farm success, dairy product labeling effects, hotel services in Libya, debt crisis impacts, capital structure determinants, leadership models, e-commerce competitor benchmarking, social media potential in healthcare, uncertainty in leasing reporting, employment structure shifts, public procurement tender prices, odds betting, corporate governance intelligence, working capital
Secondary School Offers for Sevenoaks StudentsSevenoaks ACE
Presentation showing the number of children from state primary sectors within the Sevenoaks area that were made offers to the local school, and other grammar, non-selective and faith schools in nearby towns in 2009/10 and 2010/11
The document lists the names of various types of betta fish balls, including descriptions related to color such as red, gold, green, and blue. A variety of patterns and species are also referenced, including dragon, salamander, butterfly, kitti, and dumbo. Over 50 different types of betta fish balls are named.
DF1 - ML - Vorontsov - BigARTM Topic Modelling of Large Text CollectionsMoscowDataFest
Presentation from Moscow Data Fest #1, September 12.
Moscow Data Fest is a free one-day event that brings together Data Scientists for sessions on both theory and practice.
Link: http://www.meetup.com/Moscow-Data-Fest/
This document introduces plone.api, which provides a simple Python API for common Plone development tasks. It aims to cover 20% of tasks developers do 80% of the time through clear and discoverable API methods. The goals are to keep everything in one place, stay introspectable and discoverable, and be Pythonic. It was developed using test-driven development, sprint sessions helped contribute. Examples show how it can get the portal root or check permissions in a cleaner way than before. Future work may include more methods to make additional common tasks simpler.
This document presents research on context-aware neural machine translation. It introduces a model that incorporates context from previous sentences to help with translating ambiguous words like pronouns. The model outperforms baselines on a test set involving pronouns. Analysis shows the model learns to implicitly resolve anaphora by attending to the antecedent noun phrase provided in the context. This suggests the model is capturing this discourse phenomenon without being explicitly trained for anaphora resolution.
The document is an abstracts booklet from the 18th European scientific conference of doctoral students organized by the Faculty of Business and Economics at Mendel University in Brno. It contains abstracts from papers presented at the conference across several sessions, including business economics, economics and finance, and informatics. The abstracts cover topics such as price for ski lift tickets, corporate bankruptcy determinants, machinery investment criteria, organic farm success, dairy product labeling effects, hotel services in Libya, debt crisis impacts, capital structure determinants, leadership models, e-commerce competitor benchmarking, social media potential in healthcare, uncertainty in leasing reporting, employment structure shifts, public procurement tender prices, odds betting, corporate governance intelligence, working capital
Secondary School Offers for Sevenoaks StudentsSevenoaks ACE
Presentation showing the number of children from state primary sectors within the Sevenoaks area that were made offers to the local school, and other grammar, non-selective and faith schools in nearby towns in 2009/10 and 2010/11
The document lists the names of various types of betta fish balls, including descriptions related to color such as red, gold, green, and blue. A variety of patterns and species are also referenced, including dragon, salamander, butterfly, kitti, and dumbo. Over 50 different types of betta fish balls are named.
DF1 - ML - Vorontsov - BigARTM Topic Modelling of Large Text CollectionsMoscowDataFest
Presentation from Moscow Data Fest #1, September 12.
Moscow Data Fest is a free one-day event that brings together Data Scientists for sessions on both theory and practice.
Link: http://www.meetup.com/Moscow-Data-Fest/
This document introduces plone.api, which provides a simple Python API for common Plone development tasks. It aims to cover 20% of tasks developers do 80% of the time through clear and discoverable API methods. The goals are to keep everything in one place, stay introspectable and discoverable, and be Pythonic. It was developed using test-driven development, sprint sessions helped contribute. Examples show how it can get the portal root or check permissions in a cleaner way than before. Future work may include more methods to make additional common tasks simpler.
Jarod M. Wachtel has over 5 years of legal experience in Colorado. He received his Juris Doctor from Regent University School of Law in 2014 and is licensed to practice law in Colorado. He has worked as a solo practitioner since 2015 handling both criminal and civil cases with a focus on business disputes. Prior to that, he worked as an associate trial attorney for a law office in Denver representing clients in domestic relations cases. He also completed internships during law school focusing on family law, tax law, and challenging compulsory union fees. In his free time, he volunteers providing limited legal advice through a local legal aid clinic.
The document discusses connecting to financial data sources from R for trading strategies. It covers using Yahoo Finance for end-of-day data from the past year and connecting to Interactive Brokers for intraday data through their API. It notes that IB has extensive APIs for connecting through Java and C and allows for retrieving high frequency intraday data if the necessary programming is done, though there are limits on the number of requests and no more than one year of past data can be accessed at a time.
R in finance: Introduction to R and Its Applications in FinanceLiang C. Zhang (張良丞)
This presentation is designed for experts in Finance but not familiar with R. I use some Finance applications (data mining, technical trading, and performance analysis) that you are probably most familiar with. In this short one-hour event, I focus on the "using R" rather than the Finance examples. Therefore, few interpretations of these examples will be provided. Instead, I would like you to use your field of knowledge to help yourself and hope that you can extend what you learn to other finance R packages.
Yoav Goldberg: Word Embeddings What, How and WhitherMLReview
This document discusses word embeddings and how they work. It begins by explaining how the author became an expert in distributional semantics without realizing it. It then discusses how word2vec works, specifically skip-gram models with negative sampling. The key points are that word2vec is learning word and context vectors such that related words and contexts have similar vectors, and that this is implicitly factorizing the word-context pointwise mutual information matrix. Later sections discuss how hyperparameters are important to word2vec's success and provide critiques of common evaluation tasks like word analogies that don't capture true semantic similarity. The overall message is that word embeddings are fundamentally doing the same thing as older distributional semantic models through matrix factorization.
Word embeddings are a technique for converting words into vectors of numbers so that they can be processed by machine learning algorithms. Words with similar meanings are mapped to similar vectors in the vector space. There are two main types of word embedding models: count-based models that use co-occurrence statistics, and prediction-based models like CBOW and skip-gram neural networks that learn embeddings by predicting nearby words. Word embeddings allow words with similar contexts to have similar vector representations, and have applications such as document representation.
A Simple Introduction to Word EmbeddingsBhaskar Mitra
In information retrieval there is a long history of learning vector representations for words. In recent times, neural word embeddings have gained significant popularity for many natural language processing tasks, such as word analogy and machine translation. The goal of this talk is to introduce basic intuitions behind these simple but elegant models of text representation. We will start our discussion with classic vector space models and then make our way to recently proposed neural word embeddings. We will see how these models can be useful for analogical reasoning as well applied to many information retrieval tasks.
Paper dissected glove_ global vectors for word representation_ explained _ ...Nikhil Jaiswal
This document summarizes and explains the GloVe model for generating word embeddings. GloVe aims to capture word meaning in vector space while taking advantage of global word co-occurrence counts. Unlike word2vec, GloVe learns embeddings based on a co-occurrence matrix rather than streaming sentences. It trains vectors so their differences predict co-occurrence ratios. The document outlines the key steps in building GloVe, including data preparation, defining the prediction task, deriving the GloVe equation, and comparisons to word2vec.
The document provides an introduction to word embeddings and two related techniques: Word2Vec and Word Movers Distance. Word2Vec is an algorithm that produces word embeddings by training a neural network on a large corpus of text, with the goal of producing dense vector representations of words that encode semantic relationships. Word Movers Distance is a method for calculating the semantic distance between documents based on the embedded word vectors, allowing comparison of documents with different words but similar meanings. The document explains these techniques and provides examples of their applications and properties.
Vectorization is the process of converting words into numerical representations. Common techniques include bag-of-words which counts word frequencies, and TF-IDF which weights words based on frequency and importance. Word embedding techniques like Word2Vec and GloVe generate vector representations of words that encode semantic and syntactic relationships. Word2Vec uses the CBOW and Skip-gram models to predict words from contexts to learn embeddings, while GloVe uses global word co-occurrence statistics from a corpus. These pre-trained word embeddings can then be used for downstream NLP tasks.
Talk given at the 6th Irish NLP Meetup on query understanding using conceptual slices and word embeddings.
https://www.meetup.com/NLP-Dublin/events/237998517/
The document discusses word embeddings, which learn vector representations of words from large corpora of text. It describes two popular methods for learning word embeddings: continuous bag-of-words (CBOW) and skip-gram. CBOW predicts a word based on surrounding context words, while skip-gram predicts surrounding words from the target word. The document also discusses techniques like subsampling frequent words and negative sampling that improve the training of word embeddings on large datasets. Finally, it outlines several applications of word embeddings, such as multi-task learning across languages and embedding images with text.
LDA (latent Dirichlet allocation) is a probabilistic model for topic modeling that represents documents as mixtures of topics and topics as mixtures of words. It uses the Dirichlet distribution to model the probability of topics occurring in documents and words occurring in topics. LDA can be represented as a Bayesian network. It has been used for applications like identifying topics in sentences and documents. Python packages like NLTK, Gensim, and Stopwords can be used for preprocessing text and building LDA models.
Michael Alcorn, Sr. Software Engineer, Red Hat Inc. at MLconf SF 2017MLconf
This document provides an overview of representation learning techniques used at Red Hat, including word2vec, doc2vec, url2vec, and customer2vec. Word2vec is used to learn word embeddings from text, while doc2vec extends it to learn embeddings for documents. Url2vec and customer2vec apply the same technique to learn embeddings for URLs and customer accounts based on browsing behavior. These embeddings can be used for tasks like search, troubleshooting, and data-driven customer segmentation. Duplicate detection is another application, where title and content embeddings are compared. Representation learning is also explored for baseball players to model player value.
Designing, Visualizing and Understanding Deep Neural Networksconnectbeubax
The document discusses different approaches for representing the semantics and meaning of text, including propositional models that represent sentences as logical formulas and vector-based models that embed texts in a high-dimensional semantic space. It describes word embedding models like Word2vec that learn vector representations of words based on their contexts, and how these embeddings capture linguistic regularities and semantic relationships between words. The document also discusses how composition operations can be performed in the vector space to model the meanings of multi-word expressions.
This document provides an overview of natural language processing (NLP). It discusses how NLP systems have achieved shallow matching to understand language but still have fundamental limitations in deep understanding that requires context and linguistic structure. It also describes technologies like speech recognition, text-to-speech, question answering and machine translation. It notes that while text data may seem superficial, language is complex with many levels of structure and meaning. Corpus-based statistical methods are presented as one approach in NLP.
Continuous representations of words and documents, which is recently referred to as Word Embeddings, have recently demonstrated large advancements in many of the Natural language processing tasks.
In this presentation we will provide an introduction to the most common methods of learning these representations. As well as previous methods in building these representations before the recent advances in deep learning, such as dimensionality reduction on the word co-occurrence matrix.
Moreover, we will present the continuous bag of word model (CBOW), one of the most successful models for word embeddings and one of the core models in word2vec, and in brief a glance of many other models of building representations for other tasks such as knowledge base embeddings.
Finally, we will motivate the potential of using such embeddings for many tasks that could be of importance for the group, such as semantic similarity, document clustering and retrieval.
A brief literature review on language-agnostic tokenization, covering state of the art algorithms: BPE and Unigram model. This slide is part of a weekly sharing activity.
[Emnlp] what is glo ve part ii - towards data scienceNikhil Jaiswal
GloVe is a new model for learning word embeddings from co-occurrence matrices that combines elements of global matrix factorization and local context window methods. It trains on the nonzero elements in a word-word co-occurrence matrix rather than the entire sparse matrix or individual context windows. This allows it to efficiently leverage statistical information from the corpus. The model produces a vector space with meaningful structure, as shown by its performance of 75% on a word analogy task. It outperforms related models on similarity tasks and named entity recognition. The full paper describes GloVe's global log-bilinear regression model and how it addresses drawbacks of previous models to encode linear directions of meaning in the vector space.
Jarod M. Wachtel has over 5 years of legal experience in Colorado. He received his Juris Doctor from Regent University School of Law in 2014 and is licensed to practice law in Colorado. He has worked as a solo practitioner since 2015 handling both criminal and civil cases with a focus on business disputes. Prior to that, he worked as an associate trial attorney for a law office in Denver representing clients in domestic relations cases. He also completed internships during law school focusing on family law, tax law, and challenging compulsory union fees. In his free time, he volunteers providing limited legal advice through a local legal aid clinic.
The document discusses connecting to financial data sources from R for trading strategies. It covers using Yahoo Finance for end-of-day data from the past year and connecting to Interactive Brokers for intraday data through their API. It notes that IB has extensive APIs for connecting through Java and C and allows for retrieving high frequency intraday data if the necessary programming is done, though there are limits on the number of requests and no more than one year of past data can be accessed at a time.
R in finance: Introduction to R and Its Applications in FinanceLiang C. Zhang (張良丞)
This presentation is designed for experts in Finance but not familiar with R. I use some Finance applications (data mining, technical trading, and performance analysis) that you are probably most familiar with. In this short one-hour event, I focus on the "using R" rather than the Finance examples. Therefore, few interpretations of these examples will be provided. Instead, I would like you to use your field of knowledge to help yourself and hope that you can extend what you learn to other finance R packages.
Yoav Goldberg: Word Embeddings What, How and WhitherMLReview
This document discusses word embeddings and how they work. It begins by explaining how the author became an expert in distributional semantics without realizing it. It then discusses how word2vec works, specifically skip-gram models with negative sampling. The key points are that word2vec is learning word and context vectors such that related words and contexts have similar vectors, and that this is implicitly factorizing the word-context pointwise mutual information matrix. Later sections discuss how hyperparameters are important to word2vec's success and provide critiques of common evaluation tasks like word analogies that don't capture true semantic similarity. The overall message is that word embeddings are fundamentally doing the same thing as older distributional semantic models through matrix factorization.
Word embeddings are a technique for converting words into vectors of numbers so that they can be processed by machine learning algorithms. Words with similar meanings are mapped to similar vectors in the vector space. There are two main types of word embedding models: count-based models that use co-occurrence statistics, and prediction-based models like CBOW and skip-gram neural networks that learn embeddings by predicting nearby words. Word embeddings allow words with similar contexts to have similar vector representations, and have applications such as document representation.
A Simple Introduction to Word EmbeddingsBhaskar Mitra
In information retrieval there is a long history of learning vector representations for words. In recent times, neural word embeddings have gained significant popularity for many natural language processing tasks, such as word analogy and machine translation. The goal of this talk is to introduce basic intuitions behind these simple but elegant models of text representation. We will start our discussion with classic vector space models and then make our way to recently proposed neural word embeddings. We will see how these models can be useful for analogical reasoning as well applied to many information retrieval tasks.
Paper dissected glove_ global vectors for word representation_ explained _ ...Nikhil Jaiswal
This document summarizes and explains the GloVe model for generating word embeddings. GloVe aims to capture word meaning in vector space while taking advantage of global word co-occurrence counts. Unlike word2vec, GloVe learns embeddings based on a co-occurrence matrix rather than streaming sentences. It trains vectors so their differences predict co-occurrence ratios. The document outlines the key steps in building GloVe, including data preparation, defining the prediction task, deriving the GloVe equation, and comparisons to word2vec.
The document provides an introduction to word embeddings and two related techniques: Word2Vec and Word Movers Distance. Word2Vec is an algorithm that produces word embeddings by training a neural network on a large corpus of text, with the goal of producing dense vector representations of words that encode semantic relationships. Word Movers Distance is a method for calculating the semantic distance between documents based on the embedded word vectors, allowing comparison of documents with different words but similar meanings. The document explains these techniques and provides examples of their applications and properties.
Vectorization is the process of converting words into numerical representations. Common techniques include bag-of-words which counts word frequencies, and TF-IDF which weights words based on frequency and importance. Word embedding techniques like Word2Vec and GloVe generate vector representations of words that encode semantic and syntactic relationships. Word2Vec uses the CBOW and Skip-gram models to predict words from contexts to learn embeddings, while GloVe uses global word co-occurrence statistics from a corpus. These pre-trained word embeddings can then be used for downstream NLP tasks.
Talk given at the 6th Irish NLP Meetup on query understanding using conceptual slices and word embeddings.
https://www.meetup.com/NLP-Dublin/events/237998517/
The document discusses word embeddings, which learn vector representations of words from large corpora of text. It describes two popular methods for learning word embeddings: continuous bag-of-words (CBOW) and skip-gram. CBOW predicts a word based on surrounding context words, while skip-gram predicts surrounding words from the target word. The document also discusses techniques like subsampling frequent words and negative sampling that improve the training of word embeddings on large datasets. Finally, it outlines several applications of word embeddings, such as multi-task learning across languages and embedding images with text.
LDA (latent Dirichlet allocation) is a probabilistic model for topic modeling that represents documents as mixtures of topics and topics as mixtures of words. It uses the Dirichlet distribution to model the probability of topics occurring in documents and words occurring in topics. LDA can be represented as a Bayesian network. It has been used for applications like identifying topics in sentences and documents. Python packages like NLTK, Gensim, and Stopwords can be used for preprocessing text and building LDA models.
Michael Alcorn, Sr. Software Engineer, Red Hat Inc. at MLconf SF 2017MLconf
This document provides an overview of representation learning techniques used at Red Hat, including word2vec, doc2vec, url2vec, and customer2vec. Word2vec is used to learn word embeddings from text, while doc2vec extends it to learn embeddings for documents. Url2vec and customer2vec apply the same technique to learn embeddings for URLs and customer accounts based on browsing behavior. These embeddings can be used for tasks like search, troubleshooting, and data-driven customer segmentation. Duplicate detection is another application, where title and content embeddings are compared. Representation learning is also explored for baseball players to model player value.
Designing, Visualizing and Understanding Deep Neural Networksconnectbeubax
The document discusses different approaches for representing the semantics and meaning of text, including propositional models that represent sentences as logical formulas and vector-based models that embed texts in a high-dimensional semantic space. It describes word embedding models like Word2vec that learn vector representations of words based on their contexts, and how these embeddings capture linguistic regularities and semantic relationships between words. The document also discusses how composition operations can be performed in the vector space to model the meanings of multi-word expressions.
This document provides an overview of natural language processing (NLP). It discusses how NLP systems have achieved shallow matching to understand language but still have fundamental limitations in deep understanding that requires context and linguistic structure. It also describes technologies like speech recognition, text-to-speech, question answering and machine translation. It notes that while text data may seem superficial, language is complex with many levels of structure and meaning. Corpus-based statistical methods are presented as one approach in NLP.
Continuous representations of words and documents, which is recently referred to as Word Embeddings, have recently demonstrated large advancements in many of the Natural language processing tasks.
In this presentation we will provide an introduction to the most common methods of learning these representations. As well as previous methods in building these representations before the recent advances in deep learning, such as dimensionality reduction on the word co-occurrence matrix.
Moreover, we will present the continuous bag of word model (CBOW), one of the most successful models for word embeddings and one of the core models in word2vec, and in brief a glance of many other models of building representations for other tasks such as knowledge base embeddings.
Finally, we will motivate the potential of using such embeddings for many tasks that could be of importance for the group, such as semantic similarity, document clustering and retrieval.
A brief literature review on language-agnostic tokenization, covering state of the art algorithms: BPE and Unigram model. This slide is part of a weekly sharing activity.
[Emnlp] what is glo ve part ii - towards data scienceNikhil Jaiswal
GloVe is a new model for learning word embeddings from co-occurrence matrices that combines elements of global matrix factorization and local context window methods. It trains on the nonzero elements in a word-word co-occurrence matrix rather than the entire sparse matrix or individual context windows. This allows it to efficiently leverage statistical information from the corpus. The model produces a vector space with meaningful structure, as shown by its performance of 75% on a word analogy task. It outperforms related models on similarity tasks and named entity recognition. The full paper describes GloVe's global log-bilinear regression model and how it addresses drawbacks of previous models to encode linear directions of meaning in the vector space.
Explanations in Dialogue Systems through Uncertain RDF Knowledge BasesDaniel Sonntag
We implemented a generic dialogue shell that can be configured for and applied to domain-specific dialogue applications. The dialogue system works robustly for a new domain when the application backend can automatically infer previously unknown knowledge (facts) and provide explanations for the inference steps involved. For this purpose, we employ URDF, a query engine for uncertain and potentially inconsistent RDF knowledge bases. URDF supports rule-based, first-order predicate logic as used in OWL-Lite and OWL-DL, with simple and effective top-down reasoning capabilities. This mechanism also generates explanation graphs. These graphs can then be displayed in the GUI of the dialogue shell and help the user understand the underlying reasoning processes. We believe that proper explanations are a main factor for increasing the level of user trust in end-to-end human-computer interaction systems.
Semantic Web: From Representations to ApplicationsGuus Schreiber
This document discusses semantic web representations and applications. It provides an overview of the W3C Web Ontology Working Group and Semantic Web Best Practices and Deployment Working Group, including their goals and key issues addressed. Examples of semantic web applications are also described, such as using ontologies to integrate information from heterogeneous cultural heritage sources.
Using Text Embeddings for Information RetrievalBhaskar Mitra
Neural text embeddings provide dense vector representations of words and documents that encode various notions of semantic relatedness. Word2vec models typical similarity by representing words based on neighboring context words, while models like latent semantic analysis encode topical similarity through co-occurrence in documents. Dual embedding spaces can separately model both typical and topical similarities. Recent work has applied text embeddings to tasks like query auto-completion, session modeling, and document ranking, demonstrating their ability to capture semantic relationships between text beyond just words.
This document discusses natural language inference and summarizes the key points as follows:
1. The document describes the problem of natural language inference, which involves classifying the relationship between a premise and hypothesis sentence as entailment, contradiction, or neutral. This is an important problem in natural language processing.
2. The SNLI dataset is introduced as a collection of half a million natural language inference problems used to train and evaluate models.
3. Several approaches for solving the problem are discussed, including using word embeddings, LSTMs, CNNs, and traditional bag-of-words models. Results show LSTMs and CNNs achieve the best performance.
Similar to DF1 - Py - Kalaidin - Introduction to Word Embeddings with Python (20)
DF1 - R - Natekin - Improving Daily Analysis with data.tableMoscowDataFest
Presentation from Moscow Data Fest #1, September 12.
Moscow Data Fest is a free one-day event that brings together Data Scientists for sessions on both theory and practice.
Link: http://www.meetup.com/Moscow-Data-Fest/
Presentation from Moscow Data Fest #1, September 12.
Moscow Data Fest is a free one-day event that brings together Data Scientists for sessions on both theory and practice.
Link: http://www.meetup.com/Moscow-Data-Fest/
DF1 - ML - Petukhov - Azure Ml Machine Learning as a ServiceMoscowDataFest
Presentation from Moscow Data Fest #1, September 12.
Moscow Data Fest is a free one-day event that brings together Data Scientists for sessions on both theory and practice.
Link: http://www.meetup.com/Moscow-Data-Fest/
DF1 - DL - Lempitsky - Compact and Very Compact Image DescriptorsMoscowDataFest
Presentation from Moscow Data Fest #1, September 12.
Moscow Data Fest is a free one-day event that brings together Data Scientists for sessions on both theory and practice.
Link: http://www.meetup.com/Moscow-Data-Fest/
DF1 - BD - Baranov - Mining Large Datasets with Apache SparkMoscowDataFest
Presentation from Moscow Data Fest #1, September 12.
Moscow Data Fest is a free one-day event that brings together Data Scientists for sessions on both theory and practice.
Link: http://www.meetup.com/Moscow-Data-Fest/
DF1 - BD - Degtiarev - Practical Aspects of Big Data in PharmaceuticalMoscowDataFest
Presentation from Moscow Data Fest #1, September 12.
Moscow Data Fest is a free one-day event that brings together Data Scientists for sessions on both theory and practice.
Link: http://www.meetup.com/Moscow-Data-Fest/
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...Advanced-Concepts-Team
Presentation in the Science Coffee of the Advanced Concepts Team of the European Space Agency on the 07.06.2024.
Speaker: Diego Blas (IFAE/ICREA)
Title: Gravitational wave detection with orbital motion of Moon and artificial
Abstract:
In this talk I will describe some recent ideas to find gravitational waves from supermassive black holes or of primordial origin by studying their secular effect on the orbital motion of the Moon or satellites that are laser ranged.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
Travis Hills of MN is Making Clean Water Accessible to All Through High Flux ...Travis Hills MN
By harnessing the power of High Flux Vacuum Membrane Distillation, Travis Hills from MN envisions a future where clean and safe drinking water is accessible to all, regardless of geographical location or economic status.
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
25. idea: instead of capturing co-
occurrence counts
predict surrounding words
26. Two models:
C-BOW
predicting the word given its context
skip-gram
predicting the context given a word
Explained in great detail here, so we’ll skip it for now Also see: word2vec Parameter
Learning Explained, Rong, paper
27.
28. CBOW: several times faster than skip-gram,
slightly better accuracy for the frequent words
Skip-Gram: works well with small amount of
data, represents well rare words or phrases