Neuroscience is an interdisciplinary field that studies the nervous system. The ancient Greeks were early contributors, attempting to understand the brain and explain neural disorders. In the 19th century, scientists like Broca, von Hemholtz, and Golgi made important discoveries about the structure and function of neurons. Their work led to the understanding that neurons are independent nerve cell units and laid the foundation for modern neuroscience. Today, neuroscience has many branches that study topics like emotions, behavior, language, and disorders from molecular to cognitive levels in order to better understand the brain and nervous system and develop new treatments.
Cognitive psychology emerged as a field to study mental processes like perception, attention, learning, memory, problem-solving and thinking. Early theories like structuralism and functionalism laid the foundations but had limitations. Hermann Ebbinghaus conducted pioneering research on memory through experiments on nonsense syllables, discovering the learning curve, forgetting curve and spacing effect. The information processing model views the mind as analogous to a computer, processing information through attention, perception, memory systems. Connectionism uses artificial neural networks as simplified models of the brain to study cognitive processing. Modern tools like EEG, TMS, MRI and PET provide ways to examine brain structures and activity underlying cognition.
How good is your prediction a gentle introduction to conformal prediction.Deep Learning Italia
Marco Capuccini introduces conformal prediction, a framework for assigning confidence levels to predictions from machine learning models. Conformal prediction produces a prediction set for new data instances rather than a single prediction. It guarantees the true label will be in the prediction set at least 1 - ε percent of the time, where ε is a user-specified significance level. The approach works by calibrating a model's "non-conformity measures" on labeled data, and using these to determine prediction set membership. Capuccini provides examples of applying conformal prediction to neural networks and other models. He describes using it in an application of AI-assisted pathology to generate prediction sets for histology slides with calibrated confidence.
The document discusses optimal transport and its applications to color transfer for images. It introduces discrete and continuous optimal transport, which finds the optimal way of transferring mass between distributions to minimize cost. This allows computing distances between distributions and projecting images to match color statistics. Specifically, it describes using sliced Wasserstein projections to transfer the color distribution of a source image to match that of a style image. This modified color transfer method preserves the spatial structure of the source image better than traditional histogram equalization.
Attention & Perception - Cognitive Psychology.pptxLinda M
Perception and attention are key topics in cognitive psychology. Perception involves interpreting sensory impressions to understand one's environment. Gestalt psychologists viewed perception as organized patterns or "wholes", rather than separate parts. Both bottom-up and top-down processes influence perception. Attention allows us to focus on specific stimuli while filtering out others. Selective attention experiments show people often do not consciously perceive unattended information. Divided attention tasks demonstrate limitations in performing multiple tasks simultaneously.
Neuroscience is an interdisciplinary field that studies the nervous system. The ancient Greeks were early contributors, attempting to understand the brain and explain neural disorders. In the 19th century, scientists like Broca, von Hemholtz, and Golgi made important discoveries about the structure and function of neurons. Their work led to the understanding that neurons are independent nerve cell units and laid the foundation for modern neuroscience. Today, neuroscience has many branches that study topics like emotions, behavior, language, and disorders from molecular to cognitive levels in order to better understand the brain and nervous system and develop new treatments.
Cognitive psychology emerged as a field to study mental processes like perception, attention, learning, memory, problem-solving and thinking. Early theories like structuralism and functionalism laid the foundations but had limitations. Hermann Ebbinghaus conducted pioneering research on memory through experiments on nonsense syllables, discovering the learning curve, forgetting curve and spacing effect. The information processing model views the mind as analogous to a computer, processing information through attention, perception, memory systems. Connectionism uses artificial neural networks as simplified models of the brain to study cognitive processing. Modern tools like EEG, TMS, MRI and PET provide ways to examine brain structures and activity underlying cognition.
How good is your prediction a gentle introduction to conformal prediction.Deep Learning Italia
Marco Capuccini introduces conformal prediction, a framework for assigning confidence levels to predictions from machine learning models. Conformal prediction produces a prediction set for new data instances rather than a single prediction. It guarantees the true label will be in the prediction set at least 1 - ε percent of the time, where ε is a user-specified significance level. The approach works by calibrating a model's "non-conformity measures" on labeled data, and using these to determine prediction set membership. Capuccini provides examples of applying conformal prediction to neural networks and other models. He describes using it in an application of AI-assisted pathology to generate prediction sets for histology slides with calibrated confidence.
The document discusses optimal transport and its applications to color transfer for images. It introduces discrete and continuous optimal transport, which finds the optimal way of transferring mass between distributions to minimize cost. This allows computing distances between distributions and projecting images to match color statistics. Specifically, it describes using sliced Wasserstein projections to transfer the color distribution of a source image to match that of a style image. This modified color transfer method preserves the spatial structure of the source image better than traditional histogram equalization.
Attention & Perception - Cognitive Psychology.pptxLinda M
Perception and attention are key topics in cognitive psychology. Perception involves interpreting sensory impressions to understand one's environment. Gestalt psychologists viewed perception as organized patterns or "wholes", rather than separate parts. Both bottom-up and top-down processes influence perception. Attention allows us to focus on specific stimuli while filtering out others. Selective attention experiments show people often do not consciously perceive unattended information. Divided attention tasks demonstrate limitations in performing multiple tasks simultaneously.
Writing a research report is an important part of the research process - after all, results that are not communicated to the appropriate groups are of little value. A report is a piece of scientific literature with the objective of describing and discussing research in a style that is precise, concise, and clearly written
[論文紹介] DPSNet: End-to-end Deep Plane Sweep StereoSeiya Ito
DPSNet is an end-to-end deep learning model that estimates dense depth maps from stereo image pairs. It generates cost volumes from multi-scale feature maps of reference and paired images. It then refines the cost slices with dilated convolutions considering contextual information. Finally, it regresses the depth maps from the initial and refined cost volumes. Evaluation on various datasets shows DPSNet achieves state-of-the-art performance in depth map estimation, outperforming other methods in terms of accuracy metrics while maintaining full completeness of predictions.
This document summarizes key aspects of thinking and language from Chapter 10 of the 8th edition of the psychology textbook by David Myers. It discusses concepts of thinking such as problem solving, decision making, and judgment formation. It also covers language structure including phonemes, morphemes, and grammar. Additionally, it examines language development in children from babbling to using longer phrases, and explores how language influences thinking and how thinking can occur through images. Animal thinking and language are also briefly discussed.
This document discusses item response theory and adaptive testing. It covers the item characteristic curve and how it models the probability of a correct response based on ability level. It describes one, two, and three parameter logistic models for the curve. It explains how item parameters like difficulty and discrimination are estimated by fitting the curve to observed response proportions from different ability groups. The item parameters should be group invariant, meaning they produce the same curve when estimated separately in different groups.
This document summarizes research on working memory and its components. It discusses:
1) Baddeley's multi-component model of working memory, which includes the central executive, phonological loop, visuospatial sketchpad, and episodic buffer.
2) Research showing the phonological loop and visuospatial sketchpad are specialized for verbal and visual information processing, and the episodic buffer integrates information across modalities.
3) Studies finding relationships between working memory and language, vocabulary, grammar, and literacy skills in children. Children with higher working memory capacities perform better on tasks involving these abilities.
The document summarizes a presentation on applying GANs in medical imaging. It discusses several papers on this topic:
1. A paper that used GANs to reduce noise in low-dose CT scans by training on paired routine-dose and low-dose CT images. This approach generated reconstructed low-dose CT images with improved quality.
2. A paper that used GANs for cross-modality synthesis, specifically generating skin lesion images from other modalities.
3. Additional papers discussed other medical imaging applications of GANs such as vessel-fundus image synthesis and organ segmentation.
This document discusses the basics of artificial neural networks including multi-layer perceptrons (MLPs). It explains that MLPs use multiple hidden layers between the input and output layers to extract meaningful features from the data. The document also covers topics like training neural networks using backpropagation and stochastic gradient descent, the use of mini-batches to speed up training, and common activation and loss functions.
In this project, we propose methods for semantic segmentation with the deep learning state-of-the-art models. Moreover,
we want to filterize the segmentation to the specific object in specific application. Instead of concentrating on unnecessary objects we
can focus on special ones and make it more specialize and effecient for special purposes. Furtheromore, In this project, we leverage
models that are suitable for face segmentation. The models that are used in this project are Mask-RCNN and DeepLabv3. The
experimental results clearly indicate that how illustrated approach are efficient and robust in the segmentation task to the previous work
in the field of segmentation. These models are reached to 74.4 and 86.6 precision of Mean of Intersection over Union. The visual
Results of the models are shown in Appendix part.
Achieving Algorithmic Transparency with Shapley Additive Explanations (H2O Lo...Sri Ambati
Abstract:
Explainability in the age of the EU GDPR is becoming an increasingly pertinent consideration for Machine Learning. At QuantumBlack, we address the traditional Accuracy vs. Interpretability trade-off, by leveraging modern XAI techniques such as LIME and SHAP, to enable individualised explanations without necessary limiting the utility and performance of the otherwise ‘black-box’ models. The talk focuses on Shapley additive explanations (Lundberg et al. 2017) that integrate Shapley values from the Game Theory for consistent and locally accurate explanations; provides illustrative examples and touches upon the wider XAI theory.
Bio:
Dr Torgyn Shaikhina is a Data Scientist at QuantumBlack, STEM Ambassador, and the founder of the Next Generation Programmers outreach initiative. Her background is in decision support systems for Healthcare and Biomedical Engineering with a focus on Machine Learning with limited information.
ICASSP 2018 Tutorial: Generative Adversarial Network and its Applications to ...宏毅 李
The document provides an overview of generative adversarial networks (GANs) and their applications to signal processing and natural language processing. It begins with a general introduction to GANs, including how they work, common issues, and potential solutions. Conditional GANs and unsupervised conditional GANs are also discussed. The document then outlines applications of GANs to signal processing and natural language processing.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
[DL輪読会]Domain Adaptive Faster R-CNN for Object Detection in the WildDeep Learning JP
The document discusses domain adaptive faster R-CNN for object detection. It proposes a method to adapt a model trained on labeled data from a source domain to detect objects in an unlabeled target domain. The method uses an end-to-end deep learning model with two stages. First, it reduces differences in image distributions between the source and target domains. Then it performs object detection on the target domain images using the adapted model.
This document summarizes an adversarial examples presentation. It discusses how adversarial examples are samples modified to cause misclassification, gradient descent optimization techniques, neural network training methods, and black-box and white-box adversarial attack methods like Fast Gradient Sign Method. It also covers adversarial example defenses, uses of adversarial examples in research, and targeted perturbation algorithms.
Fluid and crystallized intelligence are two factors of general intelligence originally identified by Raymond Cattell and further developed by his student John Horn. Fluid intelligence refers to reasoning and solving novel problems independently of knowledge. Crystallized intelligence refers to using skills, knowledge, and experience gained over time. While related, they are separate systems and fluid intelligence involves brain regions responsible for short-term memory and attention, while crystallized intelligence involves regions for long-term memory storage and usage. Some research questions whether training can improve fluid intelligence or if effects are only short-term and non-transferable.
Writing a research report is an important part of the research process - after all, results that are not communicated to the appropriate groups are of little value. A report is a piece of scientific literature with the objective of describing and discussing research in a style that is precise, concise, and clearly written
[論文紹介] DPSNet: End-to-end Deep Plane Sweep StereoSeiya Ito
DPSNet is an end-to-end deep learning model that estimates dense depth maps from stereo image pairs. It generates cost volumes from multi-scale feature maps of reference and paired images. It then refines the cost slices with dilated convolutions considering contextual information. Finally, it regresses the depth maps from the initial and refined cost volumes. Evaluation on various datasets shows DPSNet achieves state-of-the-art performance in depth map estimation, outperforming other methods in terms of accuracy metrics while maintaining full completeness of predictions.
This document summarizes key aspects of thinking and language from Chapter 10 of the 8th edition of the psychology textbook by David Myers. It discusses concepts of thinking such as problem solving, decision making, and judgment formation. It also covers language structure including phonemes, morphemes, and grammar. Additionally, it examines language development in children from babbling to using longer phrases, and explores how language influences thinking and how thinking can occur through images. Animal thinking and language are also briefly discussed.
This document discusses item response theory and adaptive testing. It covers the item characteristic curve and how it models the probability of a correct response based on ability level. It describes one, two, and three parameter logistic models for the curve. It explains how item parameters like difficulty and discrimination are estimated by fitting the curve to observed response proportions from different ability groups. The item parameters should be group invariant, meaning they produce the same curve when estimated separately in different groups.
This document summarizes research on working memory and its components. It discusses:
1) Baddeley's multi-component model of working memory, which includes the central executive, phonological loop, visuospatial sketchpad, and episodic buffer.
2) Research showing the phonological loop and visuospatial sketchpad are specialized for verbal and visual information processing, and the episodic buffer integrates information across modalities.
3) Studies finding relationships between working memory and language, vocabulary, grammar, and literacy skills in children. Children with higher working memory capacities perform better on tasks involving these abilities.
The document summarizes a presentation on applying GANs in medical imaging. It discusses several papers on this topic:
1. A paper that used GANs to reduce noise in low-dose CT scans by training on paired routine-dose and low-dose CT images. This approach generated reconstructed low-dose CT images with improved quality.
2. A paper that used GANs for cross-modality synthesis, specifically generating skin lesion images from other modalities.
3. Additional papers discussed other medical imaging applications of GANs such as vessel-fundus image synthesis and organ segmentation.
This document discusses the basics of artificial neural networks including multi-layer perceptrons (MLPs). It explains that MLPs use multiple hidden layers between the input and output layers to extract meaningful features from the data. The document also covers topics like training neural networks using backpropagation and stochastic gradient descent, the use of mini-batches to speed up training, and common activation and loss functions.
In this project, we propose methods for semantic segmentation with the deep learning state-of-the-art models. Moreover,
we want to filterize the segmentation to the specific object in specific application. Instead of concentrating on unnecessary objects we
can focus on special ones and make it more specialize and effecient for special purposes. Furtheromore, In this project, we leverage
models that are suitable for face segmentation. The models that are used in this project are Mask-RCNN and DeepLabv3. The
experimental results clearly indicate that how illustrated approach are efficient and robust in the segmentation task to the previous work
in the field of segmentation. These models are reached to 74.4 and 86.6 precision of Mean of Intersection over Union. The visual
Results of the models are shown in Appendix part.
Achieving Algorithmic Transparency with Shapley Additive Explanations (H2O Lo...Sri Ambati
Abstract:
Explainability in the age of the EU GDPR is becoming an increasingly pertinent consideration for Machine Learning. At QuantumBlack, we address the traditional Accuracy vs. Interpretability trade-off, by leveraging modern XAI techniques such as LIME and SHAP, to enable individualised explanations without necessary limiting the utility and performance of the otherwise ‘black-box’ models. The talk focuses on Shapley additive explanations (Lundberg et al. 2017) that integrate Shapley values from the Game Theory for consistent and locally accurate explanations; provides illustrative examples and touches upon the wider XAI theory.
Bio:
Dr Torgyn Shaikhina is a Data Scientist at QuantumBlack, STEM Ambassador, and the founder of the Next Generation Programmers outreach initiative. Her background is in decision support systems for Healthcare and Biomedical Engineering with a focus on Machine Learning with limited information.
ICASSP 2018 Tutorial: Generative Adversarial Network and its Applications to ...宏毅 李
The document provides an overview of generative adversarial networks (GANs) and their applications to signal processing and natural language processing. It begins with a general introduction to GANs, including how they work, common issues, and potential solutions. Conditional GANs and unsupervised conditional GANs are also discussed. The document then outlines applications of GANs to signal processing and natural language processing.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
[DL輪読会]Domain Adaptive Faster R-CNN for Object Detection in the WildDeep Learning JP
The document discusses domain adaptive faster R-CNN for object detection. It proposes a method to adapt a model trained on labeled data from a source domain to detect objects in an unlabeled target domain. The method uses an end-to-end deep learning model with two stages. First, it reduces differences in image distributions between the source and target domains. Then it performs object detection on the target domain images using the adapted model.
This document summarizes an adversarial examples presentation. It discusses how adversarial examples are samples modified to cause misclassification, gradient descent optimization techniques, neural network training methods, and black-box and white-box adversarial attack methods like Fast Gradient Sign Method. It also covers adversarial example defenses, uses of adversarial examples in research, and targeted perturbation algorithms.
Fluid and crystallized intelligence are two factors of general intelligence originally identified by Raymond Cattell and further developed by his student John Horn. Fluid intelligence refers to reasoning and solving novel problems independently of knowledge. Crystallized intelligence refers to using skills, knowledge, and experience gained over time. While related, they are separate systems and fluid intelligence involves brain regions responsible for short-term memory and attention, while crystallized intelligence involves regions for long-term memory storage and usage. Some research questions whether training can improve fluid intelligence or if effects are only short-term and non-transferable.
לנהל את הרגשות באינטליגנציה רגשית רציונאלית סוזי איציק מנחת קבוצותבית משפט מחוזי חיפה
ניהול רגשי - האם אנחנו מנוהלים או מנהלים את הרגשות שלנו
כידוע הרגשות הם המנוע לשינויים, עשייה, צמיחה והתפתחות. הרגשות מפעילים את הדרייב של העשייה, הם לוח הפיקוד המתריע מפני פגיעה בנו: בכבודנו, ביוקרתנו, בערכים שלנו ועוד...
ניהול רגשי נכון מתעל את הכושר הרגשי לעשייה חיובית ושמירה על איזון נפשי!
קורס אימון מנהלים והכשרת מנהלים כמאמנים ככלי לשיפור באירגונים. הצטרפו לקורס מרתק בו תקבלו כלים מעשיים למנהיגות, הנעת עובדים, שיפור האווירה ובעיקר התוצאות
תדמית או תרמית או מה באמת?
האם כרטיס הביקור שלנו משקף את מה שאנחנו באמת?
האם שפת הגוף שלנו משרתת אותנו בעת שאנחנו מציגים עמדה, או מופיעים בפני קהל? זאת ועוד, בסדנא בנושא תדמית, סוזי איציק
This document summarizes 10 effective learning techniques: elaborative interrogation, self-explanation, summarization, highlighting/underlining, keyword mnemonics, imagery for text, rereading, practice testing, distributed practice, and interleaved practice. For each technique, a brief overview is provided about how it works and its benefits for improving student learning outcomes based on evidence from cognitive and educational psychology research.
This document summarizes 10 effective learning techniques: elaborative interrogation, self-explanation, summarization, highlighting/underlining, keyword mnemonics, imagery for text, rereading, practice testing, distributed practice, and interleaved practice. For each technique, a brief overview is provided about how it works and its benefits for improving student learning outcomes based on evidence from cognitive and educational psychology research.
The document summarizes information processing and cognitive theories of learning. It discusses the Atkinson-Shiffrin model of memory which includes sensory registers, short-term memory, and long-term memory. Short-term memory is limited in capacity and duration while information is transferred to long-term memory through rehearsal and encoding. Encoding can occur through deeper levels of processing or dual coding with words and images. How information is organized and generated from memory also impacts learning.
The document discusses guidelines for making web content more accessible. It provides guidelines for making content operable, understandable, and robust. Some guidelines include providing text alternatives for non-text content, ensuring users are not trapped in content and can bypass blocks, providing clear navigation mechanisms, and ensuring the purpose of links is clear from link text or supplemental information like titles. It also provides examples of properly implementing these guidelines and warnings about potential failures to meet accessibility.
1. The document contains mathematical equations and explanations regarding atmospheric pressure, temperature, and greenhouse gases.
2. Key equations presented include the ideal gas law, Stefan-Boltzmann law of thermal radiation, and equations relating atmospheric pressure, temperature, and altitude.
3. Greenhouse gases such as CO2 and CFCs are noted to absorb infrared radiation and affect global temperatures according to the equations.
