The document discusses how the hidden bias in the training data of the Stanford Natural Language Inference (SNLI) corpus impacts performance on the recognizing textual entailment (RTE) task. The bias enables predicting entailment labels for hypothesis sentences without using premise context, violating the assumptions of the RTE task. This is due to human annotators introducing bias when composing hypothesis sentences for the SNLI corpus. Neural network models for RTE show significant performance drops on sentence pairs not affected by this bias, demonstrating the negative effect of hidden bias in training data on model performance.
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
Product Design Trends in 2024 | Teenage EngineeringsPixeldarts
The realm of product design is a constantly changing environment where technology and style intersect. Every year introduces fresh challenges and exciting trends that mold the future of this captivating art form. In this piece, we delve into the significant trends set to influence the look and functionality of product design in the year 2024.
How Race, Age and Gender Shape Attitudes Towards Mental HealthThinkNow
Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits.
To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups.
Technology
For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did.
While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis.
Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad.
Age and Gender
When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same.
Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers.
Race Affects Attitudes
As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
AI Trends in Creative Operations 2024 by Artwork Flow.pdfmarketingartwork
Creative operations teams expect increased AI use in 2024. Currently, over half of tasks are not AI-enabled, but this is expected to decrease in the coming year. ChatGPT is the most popular AI tool currently. Business leaders are more actively exploring AI benefits than individual contributors. Most respondents do not believe AI will impact workforce size in 2024. However, some inhibitions still exist around AI accuracy and lack of understanding. Creatives primarily want to use AI to save time on mundane tasks and boost productivity.
Organizational culture includes values, norms, systems, symbols, language, assumptions, beliefs, and habits that influence employee behaviors and how people interpret those behaviors. It is important because culture can help or hinder a company's success. Some key aspects of Netflix's culture that help it achieve results include hiring smartly so every position has stars, focusing on attitude over just aptitude, and having a strict policy against peacocks, whiners, and jerks.
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
Product Design Trends in 2024 | Teenage EngineeringsPixeldarts
The realm of product design is a constantly changing environment where technology and style intersect. Every year introduces fresh challenges and exciting trends that mold the future of this captivating art form. In this piece, we delve into the significant trends set to influence the look and functionality of product design in the year 2024.
How Race, Age and Gender Shape Attitudes Towards Mental HealthThinkNow
Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits.
To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups.
Technology
For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did.
While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis.
Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad.
Age and Gender
When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same.
Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers.
Race Affects Attitudes
As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
AI Trends in Creative Operations 2024 by Artwork Flow.pdfmarketingartwork
Creative operations teams expect increased AI use in 2024. Currently, over half of tasks are not AI-enabled, but this is expected to decrease in the coming year. ChatGPT is the most popular AI tool currently. Business leaders are more actively exploring AI benefits than individual contributors. Most respondents do not believe AI will impact workforce size in 2024. However, some inhibitions still exist around AI accuracy and lack of understanding. Creatives primarily want to use AI to save time on mundane tasks and boost productivity.
Organizational culture includes values, norms, systems, symbols, language, assumptions, beliefs, and habits that influence employee behaviors and how people interpret those behaviors. It is important because culture can help or hinder a company's success. Some key aspects of Netflix's culture that help it achieve results include hiring smartly so every position has stars, focusing on attitude over just aptitude, and having a strict policy against peacocks, whiners, and jerks.
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.
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.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
ESPP presentation to EU Waste Water Network, 4th June 2024 “EU policies driving nutrient removal and recycling
and the revised UWWTD (Urban Waste Water Treatment Directive)”
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
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.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
PEPSICO Presentation to CAGNY Conference Feb 2024Neil Kimberley
PepsiCo provided a safe harbor statement noting that any forward-looking statements are based on currently available information and are subject to risks and uncertainties. It also provided information on non-GAAP measures and directing readers to its website for disclosure and reconciliation. The document then discussed PepsiCo's business overview, including that it is a global beverage and convenient food company with iconic brands, $91 billion in net revenue in 2023, and nearly $14 billion in core operating profit. It operates through a divisional structure with a focus on local consumers.
Content Methodology: A Best Practices Report (Webinar)contently
This document provides an overview of content methodology best practices. It defines content methodology as establishing objectives, KPIs, and a culture of continuous learning and iteration. An effective methodology focuses on connecting with audiences, creating optimal content, and optimizing processes. It also discusses why a methodology is needed due to the competitive landscape, proliferation of channels, and opportunities for improvement. Components of an effective methodology include defining objectives and KPIs, audience analysis, identifying opportunities, and evaluating resources. The document concludes with recommendations around creating a content plan, testing and optimizing content over 90 days.
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.
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.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
ESPP presentation to EU Waste Water Network, 4th June 2024 “EU policies driving nutrient removal and recycling
and the revised UWWTD (Urban Waste Water Treatment Directive)”
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
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.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
PEPSICO Presentation to CAGNY Conference Feb 2024Neil Kimberley
PepsiCo provided a safe harbor statement noting that any forward-looking statements are based on currently available information and are subject to risks and uncertainties. It also provided information on non-GAAP measures and directing readers to its website for disclosure and reconciliation. The document then discussed PepsiCo's business overview, including that it is a global beverage and convenient food company with iconic brands, $91 billion in net revenue in 2023, and nearly $14 billion in core operating profit. It operates through a divisional structure with a focus on local consumers.
Content Methodology: A Best Practices Report (Webinar)contently
This document provides an overview of content methodology best practices. It defines content methodology as establishing objectives, KPIs, and a culture of continuous learning and iteration. An effective methodology focuses on connecting with audiences, creating optimal content, and optimizing processes. It also discusses why a methodology is needed due to the competitive landscape, proliferation of channels, and opportunities for improvement. Components of an effective methodology include defining objectives and KPIs, audience analysis, identifying opportunities, and evaluating resources. The document concludes with recommendations around creating a content plan, testing and optimizing content over 90 days.
How to Prepare For a Successful Job Search for 2024Albert Qian
The document provides guidance on preparing a job search for 2024. It discusses the state of the job market, focusing on growth in AI and healthcare but also continued layoffs. It recommends figuring out what you want to do by researching interests and skills, then conducting informational interviews. The job search should involve building a personal brand on LinkedIn, actively applying to jobs, tailoring resumes and interviews, maintaining job hunting as a habit, and continuing self-improvement. Once hired, the document advises setting new goals and keeping skills and networking active in case of future opportunities.
A report by thenetworkone and Kurio.
The contributing experts and agencies are (in an alphabetical order): Sylwia Rytel, Social Media Supervisor, 180heartbeats + JUNG v MATT (PL), Sharlene Jenner, Vice President - Director of Engagement Strategy, Abelson Taylor (USA), Alex Casanovas, Digital Director, Atrevia (ES), Dora Beilin, Senior Social Strategist, Barrett Hoffher (USA), Min Seo, Campaign Director, Brand New Agency (KR), Deshé M. Gully, Associate Strategist, Day One Agency (USA), Francesca Trevisan, Strategist, Different (IT), Trevor Crossman, CX and Digital Transformation Director; Olivia Hussey, Strategic Planner; Simi Srinarula, Social Media Manager, The Hallway (AUS), James Hebbert, Managing Director, Hylink (CN / UK), Mundy Álvarez, Planning Director; Pedro Rojas, Social Media Manager; Pancho González, CCO, Inbrax (CH), Oana Oprea, Head of Digital Planning, Jam Session Agency (RO), Amy Bottrill, Social Account Director, Launch (UK), Gaby Arriaga, Founder, Leonardo1452 (MX), Shantesh S Row, Creative Director, Liwa (UAE), Rajesh Mehta, Chief Strategy Officer; Dhruv Gaur, Digital Planning Lead; Leonie Mergulhao, Account Supervisor - Social Media & PR, Medulla (IN), Aurelija Plioplytė, Head of Digital & Social, Not Perfect (LI), Daiana Khaidargaliyeva, Account Manager, Osaka Labs (UK / USA), Stefanie Söhnchen, Vice President Digital, PIABO Communications (DE), Elisabeth Winiartati, Managing Consultant, Head of Global Integrated Communications; Lydia Aprina, Account Manager, Integrated Marketing and Communications; Nita Prabowo, Account Manager, Integrated Marketing and Communications; Okhi, Web Developer, PNTR Group (ID), Kei Obusan, Insights Director; Daffi Ranandi, Insights Manager, Radarr (SG), Gautam Reghunath, Co-founder & CEO, Talented (IN), Donagh Humphreys, Head of Social and Digital Innovation, THINKHOUSE (IRE), Sarah Yim, Strategy Director, Zulu Alpha Kilo (CA).
Trends In Paid Search: Navigating The Digital Landscape In 2024Search Engine Journal
The search marketing landscape is evolving rapidly with new technologies, and professionals, like you, rely on innovative paid search strategies to meet changing demands.
It’s important that you’re ready to implement new strategies in 2024.
Check this out and learn the top trends in paid search advertising that are expected to gain traction, so you can drive higher ROI more efficiently in 2024.
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Performance Impact Caused by Hidden Bias of Traning Data for Recognizing Textual Entailment (LREC2018)
1. Performance Impact Caused by
Hidden Bias of Training Data for RTE
Masatoshi Tsuchiya (Toyohashi University of Technology)
1
2. Brief summary of my presentation
2
The SNLI corpus, which is widely used for
English RTE task, has HIDDEN BIAS.
It enables us to estimate TE labels of
hypothesis sentences, even if no context
information given by premise sentences.
3. Definition of RTE task for SemEval/SNLI
3
A task to partition
relationships between
a premise sentence and
a hypothesis sentence
into three categories
E: Entailment
N: Neutral
C: Contradiction
Sentence
𝑠1 Two boys are
swimming in the pool.
E
𝑠2 Two girls are playing
the basketball.
N
𝑠3 Two women are
swimming in the pool.
C
ℎ Two children are
swimming in the pool.
4. Suppose the case that the same hypothesis
sentence and three premise sentences
4
When 𝑠1 is given as a
premise sentence, the
relationship between 𝑠1
and ℎ is labeled as E.
Sentence
𝑠1 Two boys are
swimming in the pool.
E
𝑠2 Two girls are playing
the basketball.
N
𝑠3 Two women are
swimming in the pool.
C
ℎ Two children are
swimming in the pool.
5. Suppose the case that the same hypothesis
sentence and three premise sentences
5
When 𝑠2 is given as a
premise sentence, the
relationship between 𝑠2
and ℎ is labeled as N.
Sentence
𝑠1 Two boys are
swimming in the pool.
E
𝑠2 Two girls are playing
the basketball.
N
𝑠3 Two women are
swimming in the pool.
C
ℎ Two children are
swimming in the pool.
6. Suppose the case that the same hypothesis
sentence and three premise sentences
6
When 𝑠3 is given as a
premise sentence, the
relationship between 𝑠3
and ℎ is labeled as C.
Sentence
𝑠1 Two boys are
swimming in the pool.
E
𝑠2 Two girls are playing
the basketball.
N
𝑠3 Two women are
swimming in the pool.
C
ℎ Two children are
swimming in the pool.
7. Suppose the case that the same hypothesis
sentence and three premise sentences
7
These examples indicate
that the TE label is
determinable
if and only if context
information is given by
a premise sentence.
Sentence
𝑠1 Two boys are
swimming in the pool.
E
𝑠2 Two girls are playing
the basketball.
N
𝑠3 Two women are
swimming in the pool.
C
ℎ Two children are
swimming in the pool.
8. (Extremely unacceptable) Null hypothesis
8
TE label of hypothesis sentences are
determinable, even if no context information
is given by premise sentences.
If this hypothesis is not rejected for a certain corpus,
the corpus has a hidden bias.
9. TE label prediction model
9
It is designed to check the null hypothesis.
It estimates TE label for hypothesis sentences, without
context information given by premise sentences.
Naive-Bayes model is employed.
𝑦 = argmax
𝑦
𝑃(𝑦)
𝑖=1
𝑛
𝑃(𝑥𝑖|𝑦)
𝑦 is a TE label
𝑥𝑖 is a feature. All word unigrams in a hypothesis sentence are
used as features.
10. Baseline model
10
Suppose the case that no information given by
either premise sentences or hypothesis sentences.
The baseline model assigns TE labels for hypothesis
sentences, only based on explicit label bias.
𝑦 = argmax
𝑦
𝑃(𝑦)
11. Explicit bias of TE labels
11
SNLI SICK
Train Devel. Test Train Devel. Test
Entailment 33.4% 33.8% 34.3% 28.9% 28.8% 28.7%
Neutral 33.3% 32.9% 32.8% 56.4% 56.4% 56.7%
Contradiction 33.4% 33.3% 33.0% 14.8% 14.8% 14.6%
The SNLI corpus is balanced for TE labels.
The SICK corpus is not balanced, and has an explicit
label bias for TE labels.
12. Statistical test of the null hypothesis
12
If the TE label prediction model
achieves statistically significant better
performance than the baseline model,
the null hypothesis is not rejected.
13. Experimental result of statistical test
13
The statistical test of the SNLI corpus does not
reject the null hypothesis.
The statistical test of the SICK corpus rejects the
null hypothesis.
Corpus TE label prediction model Baseline model
SNLI 63.3% 34.3%
SICK 56.7% 56.7%
14. Confusion matrices
14
SNLI
Predicted
labels
Corpus labels
E N C
E 2275 644 706
N 508 1976 563
C 585 599 1968
The TE label prediction model trained and tested on the
SNLI corpus tries to predict an appropriate TE label for
each individual hypothesis sentence.
The TE label prediction model trained and tested on the
SICK corpus simply outputs the major TE label, “neutral”.
SICK
Predicted
labels
Corpus labels
E N C
E 3 3 2
N 1411 2790 718
C 0 0 0
15. Intermediate conclusion of the first half
15
The null hypothesis is that TE labels of
hypothesis sentences are determinable
without context information given by
premise sentences.
It is not rejected for the SNLI corpus !
It is rejected for the SICK corpus !
Why ?
Good !
16. Topics of the latter half
16
Source of the hidden bias of the SNLI
corpus
Performance impact caused by the hidden
bias
17. Difference between
the SNLI corpus and the SICK corpus
17
Construction procedure
Both of them use Amazon Mechanical Turk.
18. Comparison of construction procedure
The SNLI corpus The SICK corpus
18
① Sentences of the Flicker
corpus are provided to
human workers as premise
sentences.
② Human workers are asked
to compose three
hypothesis sentences for
each premise sentence.
① Sentences of the Flicker
corpus are simplified using
hand-crafted rules.
② Sentence pairs selected
based on similarity are
provided to human workers.
③ Human workers are asked
to classify the sentence
pairs to three categories.
19. Difference between
the SNLI corpus and the SICK corpus
19
They are different in works asked to human workers.
For the SNLI corpus, human workers were asked to
compose hypothesis sentences.
For the SICK corpus, human workers were asked to
annotate sentence pairs.
Thus, hypothesis sentences of the SNLI corpus may
contain human bias?
20. Prominent words to estimate TE labels
20
The TE label prediction model is defined as follows:
𝑦 = argmax
𝑦
𝑃(𝑦)
𝑖=1
𝑛
𝑃(𝑥𝑖|𝑦)
𝑦 is a TE label.
𝑥𝑖 is a feature. All word unigrams of hypothesis sentence are
employed as features.
The TE label probability 𝑃(𝑦|𝑥) conditioned by a word
can be computed from this model using Bayes rule.
These prominent words are useful to estimate TE labels.
23. Examples using “nobody”
23
Premise sentences Hypothesis sentences
C
A man and a woman are standing next to
sculptures, talking while another man looks
at other sculptures.
Nobody is standing.
A woman is walking across the street eating
a banana, while a man is following with his
briefcase.
Nobody has food.
A group of young girls playing jump rope in
the street.
Nobody is playing
jump rope.
N
Three young girls posing for a picture in an
outdoor amphitheater, surrounded by adults
watching a conference.
Nobody is wearing a
hat.
E
Lacrosse players struggling for control of the
ball.
Nobody is in control
of the ball.
The manual of the SNLI corpus prohibits human workers to compose contradiction
sentences by inserting “not”. These examples suggests this prohibition is insufficient.
25. Examples using “championship”
25
Premise sentences Hypothesis sentences
N
A soccer match between a team with
white jerseys, and a team with yellow
jerseys.
The teams are in a
championship match.
Two soccer teams are competing on a
soccer field.
Two skilled soccer teams
are competing against
one another for the
championship.
There is a baseball player standing at
home plate, the catcher behind him has
his hand up in the air with his glove, and
the umpire is standing behind him, and
many people in the stands.
The final game of the
championship is being
played while many fans
are in the stands.
“Championship” is used to create unrelated entities against sport game
entities referred in premise sentences.
26. Several words provide negative clues
26
E N C
funeral 0.0081 0.3804 0.6115
stole 0.0106 0.5607 0.4287
stationary 0.2668 0.0421 0.6911
soaring 0.4340 0.0522 0.5139
human 0.6296 0.3372 0.0332
higher 0.4123 0.5477 0.0400
27. Examples using “higher”
27
Premise sentences Hypothesis sentences
E
A speed boat pulling a waterskier
along a jump.
The skier is going higher in
the water.
Top of the stands looking down at the
baseball stadium.
The baseball stadium seats
are higher than the field.
N
Two men, one in a circuit city t-shirt,
the other in an M&Ms t-shirt, operate
video game guns.
One man has a higher
score than the other .
A young smiling woman is having fun
on a rustic looking swing.
A woman is trying to swing
higher than her friend.
C
Red objects fall on men standing
behind a red wall.
The men are higher than
the wall.
Human workers can create entailment sentences and neutral sentences by
using “higher” between two entities which are referred in premise sentences.
28. The source of hidden bias
28
Human workers’ bias in their word selection
when composing hypothesis sentences.
29. Performance impact caused by
the hidden bias of the SNLI corpus
29
How much performance impact is caused by
the hidden bias of the SNLI corpus?
30. Empirical classification of the SNLI corpus
using the TE label prediction model
30
𝐸𝑒:Empirical easy test set
A subset covers all test pairs
whose TE labels are
predicted correctly by the
TE label prediction model.
𝐸𝑒 𝐻𝑒
Entailment 2,275 (36.6%) 1,093 (30.3%)
Neutral 1,976 (31.8%) 1,243 (34.5%)
Contradiction 1,968 (31.6%) 1,269 (35.2%)
6,219 3,605
𝐻𝑒:Empirical hard test set
The complement subset of 𝐸𝑒.
31. NN models for RTE task
31
Encoder-decoder model (Tim Rocktaschel et al,
ICLR2016)
Encoder using LSTM converts a premise sentence into a
vector representation.
Decoder using LSTM inferences based on the above
vector representation and a hypothesis sentence.
Attention Based Convolutional NN (Wenpeng Yin et
al, TACL2016)
Tree-based convolution model (LiLi et al, ACL2016)
34. Performance drop caused by hidden bias
34
Both NN models achieve high accuracy for the empirical easy
test set 𝐸𝑒.
However, they achieve drastic low accuracy for the empirical
hard test set 𝐻𝑒.
These results suggest that the large portion of the high
accuracy for the whole test set 𝐸𝑒 ∪ 𝐻𝑒 benefits from the
empirical easy test set 𝐸𝑒.
𝐸𝑒 ∪ 𝐻𝑒 𝐸𝑒 𝐻𝑒
Parallel LSTM model 76.8% 87.8% 57.8%
Sequential LSTM model 81.4% 90.1% 65.6%
35. Replace all premise words to UNK symbols,
to remove context information
35
label
LSTM
ℎ1
𝑊𝑒
LSTM
ℎ2
𝑊𝑒
LSTM
ℎ3
𝑊𝑒
LSTM
ℎ4
𝑊𝑒
Premise
Sentence
Hypothesis
Sentence
LSTM
𝑊𝑒
UNK
LSTM
𝑊𝑒
UNK
LSTM
𝑊𝑒
UNK
LSTM
𝑊𝑒
UNK
36. Performance of NN models for RTE
when context information is removed
36
Performance of NN models for RTE
𝐸𝑒 ∪ 𝐻𝑒 𝐸𝑒 𝐻𝑒
Parallel LSTM model 54.1% 66.0% 33.7%
Sequential LSTM model 48.6% 56.7% 34.7%
𝐸𝑒 𝐻𝑒
Entailment 2,275 (36.6%) 1,093 (30.3%)
Neutral 1,976 (31.8%) 1,243 (34.5%)
Contradiction 1,968 (31.6%) 1,269 (35.2%)
6,219 3,605
Statistics of empirical classification
They are close
to chance ratios.
They are different
to chance ratios.
37. Conclusion
37
The SNLI corpus has the hidden bias which allows us
to estimate TE labels of hypothesis sentences without
context information given by premise sentences.
NN models trained on the SNLI corpus does not
work as an RTE model for the empirical easy test set
𝐸𝑒, but work as a TE label prediction model.
39. Questions and answers in my presentation
39
Did you try more
complexed label
prediction models?
I tried a NN model and a
decision tree model for the
TE label prediction model.
However, the performance
differences between them
and the NB model were
quite small. Thus, I
reported the results of the
NB model.
How about the other data
sets?
I have evaluation results for
several corpora including
the MultiNLI corpus.
Because the MultiNLI
corpus is also constructed
in similar procedure to the
SNLI corpus, it also has a
hidden bias.
41. Examples using “proximity”
41
Promise sentences Hypothesis sentences
E
A bride and groom dance surrounded
by people at the reception.
A married couple is in the
proximity of other humans.
Many people are dunking to support
special olympics.
Several people are in close
proximity to each other.
A bull charges at a man within a
stadium while an audience watches.
Onlookers view a person
and an animal in close
proximity to each other.
N
Child playing in waves with sun on
the horizon.
A child is playing in the
water with her mother in
close proximity.
“Proximity” is convenient for human workers to compose entailment sentences
when multiple person entities appear in premise sentences.
42. Statistics of
the SNLI corpus and the SICK corpus
42
SNLI SICK
# of training pairs 55K 4500
# of development pairs 10K 500
# of test pairs 10K 4927
Vocabulary size of training pairs 36427 2178
OOV ratio of test pairs (v.s. training pairs) 0.24% 0.29%
OOV ratio of test pairs (v.s. training pairs
of the opposite corpus)
10.3% 0.15%
• SNLI training set is enough large to cover SICK test set as well as
SNLI test set.
• SICK training set covers its own test set, but does not cover SNLI
test set.