Causality in complex networks
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I explore ways to combine complex network science with the Rubin model of conference inference. In broad strokes, I discuss the difference between exogenous shocks and endogenous process, and how granularity in time can be used to tease causality out of a complex system.
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To learn more about our mission or provide feedback, take a look at www.deltanalytics.org. If you would like to use this material to further our mission of improving access to machine learning. Education please reach out to inquiry@deltanalytics.org.
To combine forecasts or to combine forecast models?
In the 46 years since the seminal work of Bates and Granger on the combination of forecasts, research has been rather consistent in concluding both empirically and theoretically, that the combination of forecasts improves accuracy over selecting a single ‘best’ forecast. However, nearly all of research has focused on the combination of forecasts ignoring the obvious question which arises; what about combining the forecast models themselves. In this paper we try to answer this question: Are there benefits to be gained from the combination of individual forecast models rather than the forecasts themselves. By exploring the combination of forecast models we focus on the combination of model elements, such as parameters and components, resulting in a single forecast model as compared to multiple forecast models whose forecasts are then combined. This approach promises savings in terms of reduced computational time in producing forecasts, and simplicity and interpretability from the use of a single (combined) forecast model, while mitigating modelling uncertainty. Using the family of exponential smoothing algorithms and the ARIMA methodology and the Bagging approach based on bootstrapping, we generated multiple forecast models (and forecasts). We assess the impact of Bagging in terms of the diversity of forecast model structures generated and investigate the use of the mean and median combination methods in combining the resulting forecasts and forecast models using real and simulated time series.
Building classification model, tree model, confusion matrix and prediction ac...
Building classification model, tree model, confusion matrix and prediction ac...National Cheng Kung University
This document discusses using classification algorithms to analyze a dataset about interest in vacations. It compares the decision tree and naive Bayes classification models based on accuracy. For the decision tree model, the accuracy was about 45% while sensitivity was around 70%. For the naive Bayes model, the accuracy was higher at about 60% and the sensitivity was 87.5%. Overall, the naive Bayes classification model had better predictive performance than the decision tree model based on this vacation interest dataset.Get hands-on with Explainable AI at Machine Learning Interpretability(MLI) Gym!
This meetup took place in Mountain View on January 24th, 2019.
Description:
With the effort and contributions from researchers and practitioners from academia and industry, Machine Learning Interpretation has become a young sub-field of ML. However, the norms around its definition and understanding is still in its infancy and there are numerous different approaches emerging rapidly. However, there seems to be a lack of a consistent explanation framework to evaluate and consistently benchmark different algorithms - evaluating against interpretation, completeness and consistency of the algorithms.
The idea with the gym is to provide a controlled interactive environment for all forms of Machine Learning algorithms, - initially focusing on supervised predictive modeling problems, to allow analysts and data-scientists to explore, debug and generate insightful understanding of the models by
1.Model Validation: Ways to explore and validate black box ML systems enabling model comparison both globally and locally - identifying biases in the training data through interpretation.
2.What-if Analysis: An interactive environment where communication can happen i.e. enable learning through interactions. User having the ability to conduct "What-If" analysis - effect of single or multiple features and their interactions
3.Model Debugging: Ways to analyze the misbehavior of the model by exploring counterfactual examples(adversarial examples and training)
4. Interpretable Models: Ability to build natively interpretable models - with the goal to simplify complex models to enable better understanding.
The central concept with MLI gym is to have an interactive environment where one could explore and simulate variations in the world(a world post a model is operationalized) beyond the defined model metrics point estimates - e.g. ROC-AUC, confusion matrix, RMSE, R2 score and others.
Speaker's Bio:
Pramit is a Lead Data Scientist/ at H2O.ai. His area of interests is building Statistical/Machine Learning models(Bayesian and Frequentist Modeling techniques) to help the business realize their data-driven goals.
Currently, he is exploring "Model Interpretation" as means to efficiently understand the true nature of predictive models to enable model robustness and security. He believes effective Model Inference coupled with Adversarial training could lead to building trustworthy models with known blind spots. He has started an open source project Skater: https://github.com/datascienceinc/Skater to solve the need for Model Inference(The project is still in its early stages of development but check it out, always eager for feedback)
Endogeneity and Entrepreneurship Research
This document discusses endogeneity in entrepreneurship research and provides practical tips for addressing it. It begins by defining endogeneity and explaining how it violates assumptions in linear models, resulting in inconsistent estimates. Common sources of endogeneity are discussed, along with myths about how to address it. The gold standard for dealing with endogeneity is randomized experiments, but instrumental variables and selection models are better options for most research. These methods are illustrated using an example looking at the relationship between risk taking and strategic learning. The document stresses the importance of properly specifying and testing for endogeneity, especially in mediation models, to avoid type I and II errors. Strong measurement models and theoretical justification of instruments can also help minimize endogeneity.
Introduction to Item Response Theory
This document provides an overview of item response theory (IRT), including key concepts like item response functions, item parameters, and assumptions of IRT models. IRT aims to measure latent traits through analysis of item-level data, allowing item and person parameters to be estimated independent of specific test administrations. The document outlines the 1, 2, and 3 parameter IRT models and how they relate item characteristics like difficulty and discrimination to the probability of endorsing an item based on trait level. Key assumptions like unidimensionality and local independence are also discussed.
The application of irt using the rasch model presnetation1
The document discusses the application of Item Response Theory (IRT) using the Rasch model to construct cognitive measures. It outlines the basics of psychometrics, Classical Test Theory, and IRT approaches like the Rasch model. The Rasch model assumes that the probability of a correct response is a function of only the person's ability and item difficulty. It has advantages over CTT like producing sample-independent item calibrations. The document reviews the Rasch model assumptions and how it can be used to identify acceptable items.
Missing Data and data imputation techniques
The document discusses techniques for imputing missing data (<NA>) in R. It introduces common imputation methods like MICE, missForest, and Hmisc. MICE creates multiple imputations using chained equations to account for uncertainty, while missForest uses random forests to impute missing values. Hmisc offers functions to impute missing values using methods like mean, regression, and predictive mean matching. The goal is to understand missing data, learn imputation methods, and choose the best approach for a given dataset.
Irt assessment
This document discusses item response theory (IRT), which is an approach used to link individuals' observed test performances to an underlying latent trait or ability that is not directly observable. The document outlines some of the key assumptions and parameters of IRT models, including local independence, unidimensionality, and certainty of response. Item parameters like difficulty, discrimination, and guessing factors are described. Advantages of IRT over classical test theory are provided in 3 sentences.
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To combine forecasts or to combine forecast models?
To combine forecasts or to combine forecast models?
Building classification model, tree model, confusion matrix and prediction ac...
Building classification model, tree model, confusion matrix and prediction ac...
Get hands-on with Explainable AI at Machine Learning Interpretability(MLI) Gym!
Get hands-on with Explainable AI at Machine Learning Interpretability(MLI) Gym!
The application of irt using the rasch model presnetation1
The application of irt using the rasch model presnetation1
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The document summarizes a research presentation on building quantum probabilistic models for decision making under uncertainty. It discusses:
1) Current Bayesian network models require manual parameter tuning and do not scale well for complex scenarios.
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10-Interpretation& Causality by Mehdi Ehtesham
This document discusses the concepts of association versus causation in epidemiology. It defines association as events occurring together more frequently than expected by chance, while causation requires proving a direct causal relationship. The document outlines different types of associations and discusses how associations can be due to chance, bias, confounding or true causation. It also discusses various criteria for establishing causality such as strength of association, consistency of findings, temporal relationship, dose-response relationship, and consideration of alternative explanations.
Complexity Informed Psychotherapy
This presentation is part of the 'Learning for Sustainable Living' online learning environment that is currently being developed at www.lfsl.info. It describes key aspects of what has been called the fifth wave in psychotherapy.
Virtual World Data Visualization
Virtual worlds have been used for interacting and building structures and simulations. The next logical step is to stream in data, represent it visually and be able to interact with it. The two main areas of virtual worlds data visualization, 3d virtual command centers and data visualization tools, are highlighted in this presentation.
DIYgenomics: An Open Platform for Democratizing the Genome
Redesigning biology may be man's ultimate artistic and scientific exploit. The first steps are reading and writing genetic data with DNA sequencing and synthetic biology. Already human genome sequencing costs have declined such that individuals worldwide are accessing their own genomic data, and can explore it through open-source science communities such as DIYgenomics.
Prediction Markets - Singularity University 2013
Prediction markets are a tool for collecting and aggregating opinion using market principles. Enterprise prediction markets are in use in 100-200 large organizations for project management and revenue forecasting. Public prediction markets are used for event prediction (election results, product sales, box office receipts).
Ibc cell therapy clinical development conference (arlington va september 10 ...
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Gregory Bonfiglio presented on why cell therapy clinical trials often fail. The document discussed cell therapy market trends and ongoing clinical trials. It noted that the overall failure rate of FDA clinical trials is 84.7%, with failures occurring most often in phases II and III due to issues with efficacy, safety, and funding. Common reasons cell therapy trials fail include technology failures to demonstrate efficacy or safety, poor clinical trial design, lack of clinical operations experience, inadequate funding, and unresolved regulatory hurdles. The document examined case studies of failed cell therapy trials from Geron and Dendreon.Virtual worlds
An overview of the metaverse and how virtual worlds have become routine for business, academic and government use. The demographics, activities and advanced applications of virtual world, particularly Second Life.
Audio: http://feeds.feedburner.com/BroaderPerspectivePodcast
Introduction to Supervised ML Concepts and Algorithms
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Lecture I
2015 Summer Institute Methods Lectures
Temporal bone1
Temporal bones are located in the skull and contain several parts - squamous, petrous, mastoid, tympanic and styloid. The petrous part contains the inner ear structures like the cochlea and semicircular canals. The mastoid part is pneumatized with air cells. The tympanic cavity contains the auditory tube, fenestrae and facial canal prominence. The temporal bone articulates with surrounding bones and contains important structures of the middle and inner ear.
Personal Genomes: what can I do with my data?
Biology evolved to be just good enough to survive and genomics provides the critical next-generation toolkit for its greater exploitation. Genomics is already starting to be medically actionable and is likely to become increasingly useful over time. This presentation discusses how your genetic information is already useful today,
Dublin2016 balancing
This document discusses balancing the reflection and assessment purposes of ePortfolios. It defines ePortfolios as both a process and product, with the process focusing on reflection and the product used for assessment. Two paradigms of assessment are described - assessment for continuous improvement, which is student-centered and formative, and assessment for accountability, which is institution-centered and summative. Finding the right balance is important to maximize benefits like engagement, deep learning, and skills while also collecting necessary assessment data. Tools and strategies are suggested to integrate accountability and improvement purposes, like using separate systems, incorporating social features, and supporting student choice and reflection.
Anterior temporal lobectomy
This document provides an overview of anterior temporal lobectomy with amygdalohippocampectomy (ATL-AH) for the treatment of epilepsy. It describes the anatomy of the temporal lobe, historical background of the surgery, patient selection criteria, preoperative evaluation including imaging and EEG patterns, surgical decision making, surgical procedure steps including positioning, craniotomy, resection of lateral and mesial temporal structures, complications, and outcomes. The surgery involves resection of the anterior temporal lobe including lateral neocortical regions and deeper mesial structures such as the hippocampus and amygdala to treat drug-resistant focal epilepsy originating in the temporal lobe.
The Poetic Prosthetic
Since reality is not fixed, because space and time are contingent, movement is possible and reality can be intentionally composed, with the aide of the poetic prosthetic. The poetic prosthetic is any technology that creates a relation of meaning between the individual and his or her possibilities for greater capability realization.
Blockchain Smartnetworks
Blockchain: a Singularity-class technology - No other technology has the power to
pull 2 billion people out of poverty overnight (with intermediary-free international remittances), produce a safe and orderly transition to the automation economy (with humans and machines in collaboration, and enacting friendly artificial intelligence), and fundamentally transform the only remaining sectors not yet re-engineered for the Internet era: economics and politics. There are growing classes of activities for smartnetwork execution, moving up the stack, pushing different qualitative states through the Internet pipes, building future smartnetworks. The smartnetworks thesis is that complex future operations will involve automated fleet coordination of “quantized” items via smartnetworks, using some kind of technology like blockchains with algorithmically-derived trust.
Facial nerve by Dr. Apoorv
The document provides details on the anatomy and clinical examination of the facial nerve (CNVII). It discusses the supranuclear, nuclear/intra-axial, and peripheral components of the facial nerve. Key points include that CNVII has four nuclei in the lower pons and exits the brainstem in two roots. It describes the intra- and extra-axial course of the nerve through the internal auditory canal, facial canal, and branches after exiting the stylomastoid foramen. Clinical tests for assessing motor, sensory, and parasympathetic function are outlined. Localization of lesions is discussed in relation to upper and lower motor neuron palsies.
Philosophy of Technology
This document summarizes a philosophy of technology lecture given by Melanie Swan. Some of the key topics discussed include contemporary philosophy of technology, the historical views of technology from Aristotle to Heidegger, issues of unthinkability and complexity in technology, different conceptions of time between humans and machines, and how this relates to an overarching theme of composable reality. The document also discusses potential applications of brain-computer interfaces and different models of human relations with technology from prosthetics to data and drones.
Hippocampus
The document summarizes key aspects of the hippocampus. It describes the hippocampus as a curved structure in the medial temporal lobe that plays important roles in memory consolidation and spatial navigation. It notes that humans and other mammals have two hippocampi, one in each side of the brain. It then details the various subregions of the hippocampus, including the Cornu Ammonis regions CA1-CA4 and the dentate gyrus. It provides information on the characteristic cell layers and connections within and between these subregions.
Managing Agility: From Complex To Simple
This is my contribution to the Pecha Kucha at the Business of Software 2009 conference. Most slides are picked from my other presentations, and there's little to no text in it.
It probably won't be useful to anyone without a video. Still, I include it here for sake of completeness.
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Introduction to Supervised ML Concepts and Algorithms
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Recently, the machine learning community has expressed strong interest in applying latent variable modeling strategies to causal inference problems with unobserved confounding. Here, I discuss one of the big debates that occurred over the past year, and how we can move forward. I will focus specifically on the failure of point identification in this setting, and discuss how this can be used to design flexible sensitivity analyses that cleanly separate identified and unidentified components of the causal model.Representing and generating uncertainty effectively presentatıon
Prof. Frank H Knight (1921) proposed that "risk" is randomness with knowable probabilities, and "uncertainty" is randomness with unknowable probabilities. However, risk and uncertainty both share features with randomness. The illustration here explains the relationship of the concepts better than words...
Neural Nets Deconstructed
Deep dive into the mathematics and algorithms of neural nets. Covers the sigmoid activation function, cross-entropy loss function, gradient descent and the derivatives used in back propagation.
Joint contrastive learning with infinite possibilities
Contrastive Learning은 두 이미지가 유사한지 유사하지 않은 지에 대해서 어떤 label이 없이 피쳐들을 배우게 하는 머신 learning 테크닉 중에 하나입니다 우리는 기존에 있는 Supervised learning과 조금 차이가 있는데 Supervised learning은 label cost가 들고
그다음에 Task specific 하기 때문에 generalizability가 조금 떨어질 수 있습니다 하지만 Contrastive Learning은 label이 없이 진행하기때문에 label cost가 없고 generalizability가 조금 더 좋을수 있습니다. 해당 논문은 보다 유용한 Contrastive Learning을 위한 Joint Contrastive Learning에 대해 제안을 하는대요 https://youtu.be/0NLq-ikBP1I
Recommendation systems in the scope of opinion formation: a model
This document summarizes a model for recommendation systems that incorporates social interactions. The model makes two main assumptions: (1) objects generate intrinsic anticipation distributions, and (2) the shift from potential to actual adoption is caused by information exchange on a social network. Mathematically, the model calculates the shifted anticipation of an item for a user based on their neighbors who have adopted, trust in those neighbors, and the intrinsic anticipation distribution. The model is able to generate user opinions in offline data and fit real-world recommendation system data.
Class9_PCA_final.ppt
PCA is a technique to reduce the dimensionality of multivariate data while retaining essential information. It works by transforming the data to a new coordinate system such that the greatest variance by any projection of the data lies on the first coordinate, called the first principal component. Subsequent components account for remaining variance while being orthogonal to previous components. PCA is performed by computing the eigenvalues and eigenvectors of the covariance matrix of the data, with the principal components being the eigenvectors. This allows visualization and interpretation of high-dimensional data in lower dimensions.
Lecture 1.pdf
This document provides an overview of a regression modelling course. It includes contact details for the lecturer and tutor. It outlines the lecture and tutorial times, textbook information, and assessment details. It also provides hints for success, including attending all classes, doing the required readings and tutorials, and using R to complete assignments. The document then begins covering key concepts in regression modelling, including the history, different types of relationships between variables, and how to construct regression models.
B. Liseo, Metodi inferenziali per dati integrati
13° Conferenza Nazionale di statistica 4-5-6 luglio 2018
#INTEGRAZIONE
Centro Congressi Ergife Via Aurelia, 619 Roma
For this assignment, use the aschooltest.sav dataset.The d
This document provides instructions for analyzing education test score data from 200 students using SPSS. It includes questions to guide analysis of relationships between test scores (dependent variable) and demographic factors like gender, race, and school type (independent variables). Students are asked to identify variables of interest, run assumption tests, conduct a one-way ANOVA and post hoc tests to address a hypothesis, and interpret the results.
XAI (IIT-Patna).pdf
This document provides a summary of explainable AI and techniques for explaining machine learning models. It discusses the motivation for explainable AI due to deep learning models being "black boxes". It covers terminology like model explanations, outcome explanations, and model inspection. Desirable properties of explanations like being concise, faithful, complete, and consistent are outlined. Techniques like model distillation and observing model training are described.
Bivariate Regression
This document discusses bivariate linear regression and its understanding. Bivariate linear regression, also called simple linear regression, involves modeling the relationship between a dependent variable (Y) and a single independent variable (X). The regression equation takes the form of Y = β0 + β1X + ε, where β0 is the intercept, β1 is the slope coefficient, and ε is the error term. This equation can be used to predict Y values based on X values, as well as understand how much variation in Y can be explained by X. Parameters β0 and β1 are estimated to maximize the explanatory power of X for Y while minimizing prediction errors.
Deep VI with_beta_likelihood
I am using DL & Actor critic tools for solving Variational inference problem. The intriguing part from my hand is that the likelihood has a Beta distribution.Thus we handle both VI issues and a non common distributions
causality_discussion_slides_final.pdf
The document discusses understanding autoencoders through interventions on latent variables. It introduces representation learning and the manifold hypothesis. Good representations are described as extensible, compact, able to extrapolate, robust, and self-aware. Disentanglement aims to maximize independence between latent variables but factors may be related. Interventional consistency focuses on the causal structure of the generative process. Probing a trained VAE using interventions reveals unexpected structure in the learned latent space beyond statistical independence of factors. Response functions and matrices are used to analyze causal links between latent variables and disentangle factors in a semantically meaningful way.
Building useful models for imbalanced datasets (without resampling)
1) Building machine learning models on imbalanced datasets, where there are many more inactive compounds than active ones, can lead to models with high accuracy but low ability to predict actives.
2) Shifting the decision threshold from 0.5 to a lower value, such as 0.2, for classifiers like random forests can significantly improve the models' ability to predict actives, as measured by Cohen's kappa, without retraining the models.
3) Across a variety of bioactivity prediction datasets, this threshold-shifting approach generally performed better than alternative methods like balanced random forests at improving predictions of active compounds.
7 QC quality control (7 QC) tools for continuous improvement of manufacturing...
This document discusses 7 quality control tools used for continuous improvement in manufacturing processes. It provides details on each tool: Pareto diagrams identify the few vital problems from many trivial ones. Cause and effect diagrams systematically identify possible causes for problems. Histograms show data distribution patterns. Control charts separate random and assignable variations. Scatter diagrams study relationships between two variables. Graphs provide pictorial data representation. When used together, these 7 tools can solve 95% of quality problems and improve manufacturing processes.
SPSS statistics - get help using SPSS
This document provides an overview of descriptive statistics, inferential statistics, and regression analysis using PASW Statistics software. It discusses topics such as frequency analysis, measures of central tendency, hypothesis testing, t-tests, ANOVA, chi-square tests, correlation, and linear regression. The document is divided into multiple parts that cover opening and manipulating data files, descriptive statistics, tests of significance, regression analysis, and chi-square/ANOVA. It also discusses importing/exporting data and using scripts in PASW Statistics.
Mixed Effects Models - Random Intercepts
There are a few potential issues with modeling the data this way:
1. Students are nested within classrooms. A student's outcomes may be more similar to others in their classroom compared to students in other classrooms, due to shared classroom factors. This violates the independence assumption of ordinary least squares regression.
2. Classroom-level factors like teacher quality are not included in the model but likely influence student outcomes. Failing to account for these could lead to omitted variable bias.
3. The error terms for students within the same classroom may not be independent as assumed, since classroom factors induce correlation.
To properly account for the nested data structure, we need to model the classroom as a second level in a multilevel
Machine Learning
Leveraging Machine Learning or IA in order to detect Credit Card Fraud and suspicious transations. The aim of this presentation is to help you to improve your knowledge in Machnie Learning and to start development of multiple families of algorithms in Python.
Data Distribution &The Probability Distributions
Explaining the concept of data types, methods of representing and distributing them using diagrams, clarifying the concept of probability, defining probability theory and methods of its distribution, explaining the basic concepts and laws of the most important distribution methods used, along with illustrative examples and graphs.
Probability plays an essential role in our daily life by predicting the possibility of an event, which is the theory that the statistician uses to help him know how well the random sample under study represents the community from which the sample is taken. Three important problems based on the rules of probability are
1. Knowledge of data types and ways of representing them represented by relative frequency.
2.Methods of estimating such as probability distributions.
3.Calculating the probability in terms of other known probabilities through operations such as union, intersection, and the laws of probability.
A Causal Framework for Meta-Analysis, drafty
This document proposes a potential outcomes framework for causal meta-analysis. It discusses issues like heterogeneity between studies and presents assumptions needed for identification. Random effects models are commonly used but may not have a clear causal interpretation. The document demonstrates through an example how including individual-level data can explain away heterogeneity between studies. Overall, it argues for establishing a solid causal foundation in meta-analysis to better understand questions being asked.
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The creators of technical infrastructure are under social and legal pressure to comply with expectations that can be difficult to translate into computational and business logics. The dissertation presented in this talk bridges this gap through three projects that focus on privacy engineering, information security, and data economics, respectively. These projects culminate in a new formal method for evaluating the strategic and tactical value of data. This method relies on a core theoretical contribution building on the work of Shannon, Dretske, Pearl, Koller, and Nissenbaum: a definition of information flow as a channel situated in a context of causal relations.
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I gave this talk at UC Berkeley School of Information's research seminar on Information and Communication Technology for Development (ICTD).
Much of the material comes from an older presentation I wrote with Rolando Peñate.
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The document discusses GeoNode, an open source software project that aims to build a next generation Spatial Data Infrastructure (SDI) based on best practices from modern web services. Some key points:
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JAMES WEBB STUDY THE MASSIVE BLACK HOLE SEEDS
The pathway(s) to seeding the massive black holes (MBHs) that exist at the heart of galaxies in the present and distant Universe remains an unsolved problem. Here we categorise, describe and quantitatively discuss the formation pathways of both light and heavy seeds. We emphasise that the most recent computational models suggest that rather than a bimodal-like mass spectrum between light and heavy seeds with light at one end and heavy at the other that instead a continuum exists. Light seeds being more ubiquitous and the heavier seeds becoming less and less abundant due the rarer environmental conditions required for their formation. We therefore examine the different mechanisms that give rise to different seed mass spectrums. We show how and why the mechanisms that produce the heaviest seeds are also among the rarest events in the Universe and are hence extremely unlikely to be the seeds for the vast majority of the MBH population. We quantify, within the limits of the current large uncertainties in the seeding processes, the expected number densities of the seed mass spectrum. We argue that light seeds must be at least 103 to 105 times more numerous than heavy seeds to explain the MBH population as a whole. Based on our current understanding of the seed population this makes heavy seeds (Mseed > 103 M⊙) a significantly more likely pathway given that heavy seeds have an abundance pattern than is close to and likely in excess of 10−4 compared to light seeds. Finally, we examine the current state-of-the-art in numerical calculations and recent observations and plot a path forward for near-future advances in both domains.
Discovery of An Apparent Red, High-Velocity Type Ia Supernova at 𝐳 = 2.9 wi...
We present the JWST discovery of SN 2023adsy, a transient object located in a host galaxy JADES-GS
+
53.13485
−
27.82088
with a host spectroscopic redshift of
2.903
±
0.007
. The transient was identified in deep James Webb Space Telescope (JWST)/NIRCam imaging from the JWST Advanced Deep Extragalactic Survey (JADES) program. Photometric and spectroscopic followup with NIRCam and NIRSpec, respectively, confirm the redshift and yield UV-NIR light-curve, NIR color, and spectroscopic information all consistent with a Type Ia classification. Despite its classification as a likely SN Ia, SN 2023adsy is both fairly red (
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(
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−
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)
∼
0.9
) despite a host galaxy with low-extinction and has a high Ca II velocity (
19
,
000
±
2
,
000
km/s) compared to the general population of SNe Ia. While these characteristics are consistent with some Ca-rich SNe Ia, particularly SN 2016hnk, SN 2023adsy is intrinsically brighter than the low-
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Ca-rich population. Although such an object is too red for any low-
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cosmological sample, we apply a fiducial standardization approach to SN 2023adsy and find that the SN 2023adsy luminosity distance measurement is in excellent agreement (
≲
1
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) with
Λ
CDM. Therefore unlike low-
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Ca-rich SNe Ia, SN 2023adsy is standardizable and gives no indication that SN Ia standardized luminosities change significantly with redshift. A larger sample of distant SNe Ia is required to determine if SN Ia population characteristics at high-
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truly diverge from their low-
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counterparts, and to confirm that standardized luminosities nevertheless remain constant with redshift.
Summary Of transcription and Translation.pdf
Hello Everyone Here We are Sharing You with The process of protien Synthesis in very short points you will be Able to understand It Very well
The cost of acquiring information by natural selection
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
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Wellprovedthatnogranariescanbefilledwithgrainswithoutinsectsastheharvestedproducecontainegg(or)larvae(or)pupae(or)adultinsectinthembecauseoffieldcarryoverinfestationwhichcannotbeavoidedindevelopingcountrieslikeIndia
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Anti-Universe And Emergent Gravity and the Dark Universe
Recent theoretical progress indicates that spacetime and gravity emerge together from the entanglement structure of an underlying microscopic theory. These ideas are best understood in Anti-de Sitter space, where they rely on the area law for entanglement entropy. The extension to de Sitter space requires taking into account the entropy and temperature associated with the cosmological horizon. Using insights from string theory, black hole physics and quantum information theory we argue that the positive dark energy leads to a thermal volume law contribution to the entropy that overtakes the area law precisely at the cosmological horizon. Due to the competition between area and volume law entanglement the microscopic de Sitter states do not thermalise at sub-Hubble scales: they exhibit memory effects in the form of an entropy displacement caused by matter. The emergent laws of gravity contain an additional ‘dark’ gravitational force describing the ‘elastic’ response due to the entropy displacement. We derive an estimate of the strength of this extra force in terms of the baryonic mass, Newton’s constant and the Hubble acceleration scale a0 = cH0, and provide evidence for the fact that this additional ‘dark gravity force’ explains the observed phenomena in galaxies and clusters currently attributed to dark matter.
SDSS1335+0728: The awakening of a ∼ 106M⊙ black hole⋆
Context. The early-type galaxy SDSS J133519.91+072807.4 (hereafter SDSS1335+0728), which had exhibited no prior optical variations during the preceding two decades, began showing significant nuclear variability in the Zwicky Transient Facility (ZTF) alert stream from December 2019 (as ZTF19acnskyy). This variability behaviour, coupled with the host-galaxy properties, suggests that SDSS1335+0728 hosts a ∼ 106M⊙ black hole (BH) that is currently in the process of ‘turning on’. Aims. We present a multi-wavelength photometric analysis and spectroscopic follow-up performed with the aim of better understanding the origin of the nuclear variations detected in SDSS1335+0728. Methods. We used archival photometry (from WISE, 2MASS, SDSS, GALEX, eROSITA) and spectroscopic data (from SDSS and LAMOST) to study the state of SDSS1335+0728 prior to December 2019, and new observations from Swift, SOAR/Goodman, VLT/X-shooter, and Keck/LRIS taken after its turn-on to characterise its current state. We analysed the variability of SDSS1335+0728 in the X-ray/UV/optical/mid-infrared range, modelled its spectral energy distribution prior to and after December 2019, and studied the evolution of its UV/optical spectra. Results. From our multi-wavelength photometric analysis, we find that: (a) since 2021, the UV flux (from Swift/UVOT observations) is four times brighter than the flux reported by GALEX in 2004; (b) since June 2022, the mid-infrared flux has risen more than two times, and the W1−W2 WISE colour has become redder; and (c) since February 2024, the source has begun showing X-ray emission. From our spectroscopic follow-up, we see that (i) the narrow emission line ratios are now consistent with a more energetic ionising continuum; (ii) broad emission lines are not detected; and (iii) the [OIII] line increased its flux ∼ 3.6 years after the first ZTF alert, which implies a relatively compact narrow-line-emitting region. Conclusions. We conclude that the variations observed in SDSS1335+0728 could be either explained by a ∼ 106M⊙ AGN that is just turning on or by an exotic tidal disruption event (TDE). If the former is true, SDSS1335+0728 is one of the strongest cases of an AGNobserved in the process of activating. If the latter were found to be the case, it would correspond to the longest and faintest TDE ever observed (or another class of still unknown nuclear transient). Future observations of SDSS1335+0728 are crucial to further understand its behaviour. Key words. galaxies: active– accretion, accretion discs– galaxies: individual: SDSS J133519.91+072807.4
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Causality in complex networks
- 1. Causality in Complex Networks Sebastian Benthall D-Lab
- 2. Why causality? Causal relations are scientifically interesting because when exposed, they are: ● a reliable mechanism ● that supports intervention By understanding causality, we can predict and control. This is the interest behind causal knowledge.
- 3. Why this talk? ● A “working group” talk - I don’t claim to be an expert ● I’m working through research problems that are challenging to me ● I’m presenting this work in progress both to inform and solicit feedback ● You are welcome to do the same with your work!
- 4. Potential outcomes The Rubin Causal Model (RCM) or potential outcomes framework is ascendant.
- 5. Potential outcomes “The causal effect of a treatment on a single individual or unit of observation is the comparison (e.g., difference) between the value of the outcome if the unit is treated and the value of the outcome if the unit is not treated.” (Angrist, Imbens, and Rubin, 1996)
- 6. Potential outcomes “The causal effect of a treatment on a single individual or unit of observation is the comparison (e.g., difference) between the value of the outcome if the unit is treated and the value of the outcome if the unit is not treated.” (Angrist, Imbens, and Rubin, 1996)
- 7. Potential outcomes Average effect: (10+9+7+12)/4 = 9.5 Controlled Outcome - Yi (0) Treated Outcome - Yi (1) Causal Effect of Treatment Yi (1) - Y(0) Alice 20 30 10 Bob 15 24 9 Cathy 10 17 7 David 22 34 12
- 8. Potential outcomes You only ever see some of these. This has been called the Fundamental Problem of Causal Inference Controlled Outcome - Yi (0) Treated Outcome - Yi (1) Causal Effect of Treatment Yi (1) - Y(0) Alice 20 30 10 Bob 15 24 9 Cathy 10 17 7 David 22 34 12
- 9. Potential outcomes Stable Unit Treatment Value Assumption "the [potential outcome] observation on one unit should be unaffected by the particular assignment of treatments to the other units"
- 10. Potential outcomes Controlled Outcome - Yi (0) Treated Outcome - Yi (1) Causal Effect of Treatment Yi (1) - Y(0) Alice 20 30 10 Bob, if Alice in not treated 15 24 9 Bob, if Alice is treated 18 29 11 Cathy 10 17 7 David 22 34 12
- 11. Potential outcomes So for every unit, we have to map out all the variables that can have an effect on the potential outcomes. Spouse treated Unit treated Outcome
- 12. Potential outcomes So for every unit, we have to map out all the variables that can have an effect on the potential outcomes. A great tool for this: Pearl’s causal networks.
- 13. Causal networks Pearl, Causality, http://bayes.cs.ucla.edu/BOOK-2K/
- 14. Note There are differences between Pearl and Rubin’s frameworks but their core concepts are compatible, so says Andrew Gelman, 2009: http://andrewgelman.com/2009/07/07/more_on_pearls/
- 15. Causal networks N.B. An interesting thing about causal networks is that the conditional probability distributions can be arbitrarily complex. Also, variables need not just be whole numbers or scalars. They could be a matrix.
- 16. Complex networks Now I’m going to talk about networks that are not causal networks. Sometimes in the literature these are called complex networks.
- 20. Complex networks There are lots of different kinds of networks observed in nature and society. They can differ substantially in their emergent properties.
- 21. Complex networks def: emergent property “An emergent property is a property which a collection or complex system has, but which the individual members do not have. A failure to realize that a property is emergent, or supervenient, leads to the fallacy of division.”
- 24. How to make a graph Different processes for generating graphs have result in graphs with different properties.
- 25. How to make a graph ● Erdős–Rényi (ER) model: G(n,p): Create n nodes and create edges with probability p ● Barabási–Albert (BA) model: ○ Begin with a fully connected network of m0 nodes ○ Each new node is connected to m (< m0 ) node probability proportional to degree ki of each node i
- 26. How to predict a graph The distribution of degree of an Erdős–Rényi graph is binomial. The distribution of degree of a Barabási– Albert (BA) graph is scale-free/power-law.
- 27. Bayes Theorem Recall Bayes Theorem: P(H|D) ∝ P(D|H) P(H)
- 28. Bayes Theorem Recall Bayes Theorem: P(H|D) ∝ P(D|H) P(H) If we can show a difference in the likelihood of data under different hypotheses, we can learn something
- 29. What process created this graph?
- 30. What process created this graph? Histogram of observed node degrees d
- 31. What process created this graph? Can in principle compare P(d|ER) and P(d|BA).
- 32. What process created this graph? ● We can in principle statistically distinguish hypotheses about generative processes based on emergent properties. ● Is this a causal inference?
- 33. Ways to model this Process Graph
- 34. Ways to model this All the logic of graph generation is buried in the conditional probability function P(B|A). A Process B Graph
- 35. Ways to model this All the logic of graph generation is buried in the conditional probability function P(B|A). “Logical causation” allows no intervention! A Process B Graph
- 36. Ways to model this The Barabási–Albert (BA) model suggests this interpretation of graph changing over time. You can model time series data like this. B0 B1 B2 B3 B4
- 37. Ways to model this Now we can model a stable transition function within the graph and external causes. B0 B1 B2 B3 B4 C
- 38. Ways to model this In other words, endogenous process + exogenous shocks B0 B1 B2 B3 B4 C
- 39. Tools in the toolbox If we want to understand the effect of a kind of exogenous shock, and we know the endogenous process, then we can look for natural experiments that expose the treated outcome.
- 40. Tools in the toolbox To know the endogenous process, then compute the likelihood of emergent properties. B0 B1 B2 B3 B4 EEE E E
- 41. Problems ● The vastness of the hypothesis space of graph generation processes ● Related: how do you choose a prior?
- 42. Problems ● Computing the likelihood of a graph’s emergent properties given a particular generation process is ○ tricky math ○ maybe computationally hard
- 43. Dissatisfactions ● We have continued to bury much of the mechanism of interest in the conditional probability function. ● Suppose we want to cash this out as a finer- grained mechanism that supports finer interventions ● Can we think at multiple levels of abstraction at once?
- 44. Thanks! Contact me at: sb at ischool dot berkeley dot edu