text:
# The mycelium of a multicelled fungus is a mesh of filaments, each called a ______a. septab. hyphac. spore

###### Question:

The mycelium of a multicelled fungus is a mesh of filaments, each called a ______ a. septa b. hypha c. spore

#### Similar Solved Questions

##### Which stereoisomer of 3-hexene forms (3S,4S)-4-bromo-3-hexanol or (3R,4R)-4-bromo-3-hexanol when it reacts with Brz and H20? Draw the molecule on the canvas by choosing buttons from the Tools (for bonds); Atoms, and Advanced Template toolbars The single bond is active by default:Ht p 0 0M ; G 7%Marvin JSChemAon
Which stereoisomer of 3-hexene forms (3S,4S)-4-bromo-3-hexanol or (3R,4R)-4-bromo-3-hexanol when it reacts with Brz and H20? Draw the molecule on the canvas by choosing buttons from the Tools (for bonds); Atoms, and Advanced Template toolbars The single bond is active by default: Ht p 0 0 M ; G 7 %...
##### Please solve this question. (history of math) 7. Approximating Square Roots. Heron of Alexandria came up...
please solve this question. (history of math) 7. Approximating Square Roots. Heron of Alexandria came up with the following iterative procedure for approximating square roots. Suppose you want to find and you have that A = a + b where al is the perfect square nearest A. Then you start by averaging a...
##### How do you solve x=5 and x+y=8?
How do you solve x=5 and x+y=8?...
##### Question 1: (20 points) Find the general solution of the differential equation1-y" | (3r2 4r)y' + (-6x + 6)y = 1"'ek given that y1() = 12 is solution of the corresponding homogeneous equation:
Question 1: (20 points) Find the general solution of the differential equation 1-y" | (3r2 4r)y' + (-6x + 6)y = 1"'ek given that y1() = 12 is solution of the corresponding homogeneous equation:...
##### Sets 4 and B are subsets of the universal set U. These sets are defined as follows_U=(f.km.s.x.y.z } 4=(s.X.y.2 } B = { k;m.s.x.y } Find the following sets Write your answer in roster form or as @_(4U B)'(b) 4'nB =
Sets 4 and B are subsets of the universal set U. These sets are defined as follows_ U=(f.km.s.x.y.z } 4=(s.X.y.2 } B = { k;m.s.x.y } Find the following sets Write your answer in roster form or as @_ (4U B)' (b) 4'nB =...
##### Show that a triangular symmetric matrix is diagonal.
Show that a triangular symmetric matrix is diagonal....

format: plain


text:
This project was the capstone project for MIT’s Analytics Edge course and offered on EdX. The competition itself was hosted on Kaggle. The objecive of the competition was to predict whether a NY Times article would be popular. My model predicted the correct outcome with 89% accuracy which was only 3 percent behind the competition winner. You can see the analysis in the link above or by installing the package and reading the vignette.

devtools::install_github("kuhnrl30/NYTimes")
library(NYTimes)
vignette(NYTImes)


text:
### Neyman’s Smoothness Test: a Trade-off between Moment-based and Distribution-based Leakage Detections

<NAME>, <NAME>, and <NAME>

##### Abstract

Leakage detection tests have become an indispensable tool for testing implementations featuring side channel countermeasures such as masking. Whilst moment-based techniques such as the Welch’s t-test are universally powerful if there is leakage in a central moment, they naturally fail if this is not the case. Distribution-based techniques such as the χ2-test then come to the rescue, but they have shown not to be robust with regards to noise. In this paper, we propose a novel leakage detection technique based on Neyman’s smoothness test. We find that our new test is robust with respect to noise (similar to the merit of Welch’s t-test), and can pick up on leakage that is not located in central moments (similar to the merit of the χ2-test). We also find that there is a sweet-spot where Neyman’s test outperforms both the t-test and the χ2-test. Realistic measurements confirm that such a sweet-spot is relevant in practice for detecting implementation flaws.

Available format(s)
Category
Implementation
Publication info
Published elsewhere. MINOR revision.IEEE TIFS
Keywords
Leakage detectionNeyman's smoothness test
Contact author(s)
si-gao @ outlook com
elisabeth oswald @ aau at
yanyansmajesty @ outlook com
History
Short URL
https://ia.cr/2021/1039

CC BY

BibTeX

@misc{cryptoeprint:2021/1039,
author = {<NAME> and <NAME> and <NAME>},
title = {Neyman’s Smoothness Test: a Trade-off between Moment-based and Distribution-based Leakage Detections},
howpublished = {Cryptology ePrint Archive, Paper 2021/1039},
year = {2021},
note = {\url{https://eprint.iacr.org/2021/1039}},
url = {https://eprint.iacr.org/2021/1039}
}

Note: In order to protect the privacy of readers, eprint.iacr.org does not use cookies or embedded third party content.


text:
# What Are The Data Sets And Evaluation Criteria To Fairly Evaluate Defense Mechanisms?

3 main points
✔ We find that different images have different robustness to hostile attacks
✔ Defense mechanisms evaluated on images that are robust, to begin with, do not perform correctly
✔ Proposed a dataset for fair benchmarking and a set of evaluation criteria

written by <NAME> <NAME>
(Submitted on 5 Nov 2020 (v1), last revised 7 Nov 2020 (this version, v2))

Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
code：

## Outline of Research

Recently, there has been a lot of research on adversarial attacks, in which the input data of a machine learning model is manipulated to produce false results. In this kind of research, dataset bias is a problem, especially in the evaluation of defense methods. This is because it is not possible to determine whether the robustness obtained during the evaluation is due to the dataset or to the defense method. In this paper, we propose a method to identify robust data and data for which accuracy recovery is likely to occur by applying defensive methods, and we propose a dataset called ImageNet-R, which is a collection of robust data. In addition, we proposed three metrics to measure the robustness of the data. This allows us to perform unbiased benchmarking of adversarial attack and defense algorithms.

## Related research

### What is a hostile attack?

An adversarial attack is an attack that misleads the output of a model by tampering with the input data. Since we are discussing image data in this paper, we will discuss image data in the following sections.

Adversarial attacks on image data mislead the output of the model by adding noise to the input image that is indistinguishable to a human observer. The method of finding this noise is the type of attack method. For more details, please refer to this article.

### Methods of defending against hostile attacks

Various methods have been proposed to defend against adversarial attacks. Broadly speaking.

1. hostile learning
2. Searching for Robust Architectures
3. Image preprocessing

These three are considered to be the most promising. Adversarial learning aims to create a model that is robust to adversarial attacks by including samples created by adversarial attacks (adversarial samples) when training the network. Although this method is very effective, it is prone to overfitting, and thus there is extensive research on how to solve this problem.

Since there is no unified evaluation standard for any of the defense methods, if the evaluation is done on originally robust data, the performance will be higher than it should be.

## Defense-friendly data sets

In this section, we investigate the properties of datasets that are inherently robust to adversarial attacks or effective against defensive methods against adversarial attacks. The authors identified three types of data that are easy to classify.

1. Easy Image: no perturbations, same classification results for all models
2. ε-robust Images: all models classified correctly even with perturbation without defense algorithm
3. Defense-friendly: Accuracy recovery is large when using defense algorithms.

The perturbation here refers to the noise that is added when a hostile attack is performed. The larger the perturbation added, the stronger the attack, but the more the image changes, the easier it is to see with the human eye.

An example of these datasets might look something like this

The ε-robust Images will be the images generated by the attack called PGD on Easy Images that all models correctly classified without any defense algorithm. Since 15554 images were correctly classified when $\epsilon = 0.01$, we treat them as a robust dataset.

## Criteria for evaluating the robustness of a data set

In this section, we describe the evaluation criteria for the robustness of the data itself. The authors proposed three criteria for this evaluation.

ARD represents the fraction of the dataset that is robust given an attack and a model with perturbation ε. AMP represents the smallest perturbation that makes a particular attack on the model M irresistible. The larger this value is, the easier the defense is; ADF represents the fraction of images in the dataset that can be recovered using the defense against a small ε attack. The restoration here refers to the data that has been incorrectly output by the attack and can now be correctly classified by the defense method.

We created six different subsets (robust and non-robust images) using randomly selected images from the dataset we had. The results of ARD, AMP, and ADR scores applied to these datasets are shown below.

NR is a non-robust image and R is a robust image. As you can see, the ARD score and ADF score are higher for the robust images.

Although we have found these scores to be valid metrics, these scores are computationally expensive, so we need metrics that can be computed more efficiently. Therefore, we solve this problem by creating a model that classifies robust and non-robust using predictions.

In prediction, the results of training with the traditional ML model and the deep learning model are as follows.

The deep learning model used CNN to extract the image features, while the ML model used GLCM, a statistical feature extraction method. It was expected that CNN, a newer method, would provide by far the best accuracy, but the ML model, the best performer, achieved an accuracy of 75 using only statistical features extracted from grayscale images. The grayscale image contains the Y channel from YCbCr. The results show that a model using GLCM features extracted from the Y channel can recognize whether an image is robust or not in the majority of cases. It is important to note that the deep learning model uses RGB images, while the ML model is a grayscale image. Therefore, from the point of view of building a simple predictive model, a model using GLCM features extracted from the Y channel is more appropriate.

## Summary

In this paper, we show the existence of defensible images that are resilient to hostile attacks and recover the accuracy of the attacked model more easily than other images. Since defensive methods evaluated on datasets containing many such images will be overestimated, we proposed metrics to evaluate the robustness of the datasets. To reduce the computational complexity of these metrics, we also proposed a method that uses prediction to determine whether an image is robust or non-robust.

The point of view that defensive methods are overestimated if the dataset itself is robust in the first place is very interesting, and it is expected that research on defensive methods will take this point of view into account in the future.

If you have any suggestions for improvement of the content of the article,


text:
# Seminar of Algebra

### Towards homotopy theory of model categories

Speaker:
<NAME> (University of Haifa)
Email:
<EMAIL>
Location:
Departamento de Álgebra
Date:
Mon, 28 mar 2016 12:30
<NAME> is a lecturer at the University of Haifa at Oranim. His main area of interest is Homotopy theory. More specifically, homotopical localizations, Goodwillie calculus, and Brown representability.

We will show that every Quillen equivalence of two (simplicial, combinatorial) model categories A and B with all objects cofibrant induces a Quillen equivalence of the categories of homotopy functors from A and B into simplicial sets.

This theorem may be viewed as model theoretic interpretation of a theorem by Dwyer and Kan. On the other hand, it indicates that there should exist a sound homotopy theory of model categories.

We intend to discuss why homotopy theory of homotopy theories (presented nowadays in plenty different models) does not give a satisfactory description of homotopy theory of model categories.


text:
# Insert pdf file in latex

I want to insert PDF or doc file as appendix in my latex file. Do you know what I do?

### #1 building

\includegraphics{myfig.pdf}

### #2 building

Use pdfpages Bag.

\usepackage{pdfpages}

To include all pages in a PDF file:

\includepdf[pages=-]{myfile.pdf}

First page of PDF only:

\includepdf[pages={1}]{myfile.pdf}

Run texdoc pdfpages in the shell to see the full manual of pdfpages.

### #3 building

I don't think there will be an automatic way. You may also need to add page numbers to the appendix correctly. If you already have a PDF document with multiple pages, you must first extract the PDF document of each page using Adobe Acrobat Professional and save each page as a separate PDF file. Then, you must include the image base of each PDF document as the previous page (each page), and use NEWPAGE, G,

\appendix
\section{Quiz 1}\label{sec:Quiz}
\begin{figure}[htp] \centering{
\includegraphics[scale=0.82]{quizz.pdf}}
\caption{Experiment 1}
\end{figure}

\newpage
\section{Sample paper}\label{sec:Sample}
\begin{figure}[htp] \centering{
\includegraphics[scale=0.75]{sampaper.pdf}}
\caption{Experiment 2}
\end{figure}

Now each page displays a pdf image, with the correct page number at the bottom. As my example shows, you must use the scale factor for each image to fit the correct size of a single page. I hope it helps

### #4 building

To put the entire pdf in your file instead of just one page, use:

\usepackage{pdfpages}

\includepdf[pages=-]{myfile.pdf}

### #5 building

There is an add-on package that does not work under pdflatex

\begin{figure}[h]
\centering
\includegraphics[width=\ScaleIfNeeded]{figuras/diagrama-spearman.pdf}
\caption{Schematical view of Spearman's theory.}
\end{figure}

"Diagramma Spearman. pdf" is a graph generated with TikZ, which is the code (it is another. tex file, different from the. tex file I want to insert pdf)

\documentclass[border=3mm]{standalone}
\usepackage[applemac]{inputenc}
\usepackage[protrusion=true,expansion=true]{microtype}
\usepackage[bb=lucida,bbscaled=1,cal=boondoxo]{mathalfa}
\usepackage[stdmathitalics=true,math-style=iso,lucidasmallscale=true,romanfamily=bright]{lucimatx}
\usepackage{tikz}
\usetikzlibrary{intersections}
\newcommand{\at}{\makeatletter @\makeatother}

\begin{document}

\begin{tikzpicture}
\tikzset{venn circle/.style={draw,circle,minimum width=5cm,fill=#1,opacity=1}}
\node [venn circle = none, name path=A] (A) at (45:2cm) { };
\node [venn circle = none, name path=B] (B) at (135:2cm) { };
\node [venn circle = none, name path=C] (C) at (225:2cm) { };
\node [venn circle = none, name path=D] (D) at (315:2cm) { };
\node[above right] at (barycentric cs:A=1) {logical};
\node[above left] at (barycentric cs:B=1) {mechanical};
\node[below left] at (barycentric cs:C=1) {spatial};
\node[below right] at (barycentric cs:D=1) {arithmetical};
\node at (0,0) {G};
\end{tikzpicture}

\end{document}

This is the chart I included

Keywords: shell


text:
## CryptoDB

### Paper: Efficient Collision Attack Frameworks for RIPEMD-160

Authors: <NAME> <NAME> <NAME> DOI: 10.1007/978-3-030-26951-7_5 (login may be required) Search ePrint Search Google RIPEMD-160 is an ISO/IEC standard and has been applied to generate the Bitcoin address with SHA-256. Due to the complex dual-stream structure, the first collision attack on reduced RIPEMD-160 presented by Liu, Mendel and Wang at Asiacrypt 2017 only reaches 30 steps, having a time complexity of $2^{70}$. Apart from that, several semi-free-start collision attacks have been published for reduced RIPEMD-160 with the start-from-the-middle method. Inspired from such start-from-the middle structures, we propose two novel efficient collision attack frameworks for reduced RIPEMD-160 by making full use of the weakness of its message expansion. Those two frameworks are called dense-left-and-sparse-right (DLSR) framework and sparse-left-and-dense-right (SLDR) framework. As it turns out, the DLSR framework is more efficient than SLDR framework since one more step can be fully controlled, though with extra $2^{32}$ memory complexity. To construct the best differential characteristics for the DLSR framework, we carefully build the linearized part of the characteristics and then solve the corresponding nonlinear part using a guess-and-determine approach. Based on the newly discovered differential characteristics, we provide colliding messages pairs for the first practical collision attacks on 30 and 31 (out of 80) steps of RIPEMD-160 with time complexity $2^{35.9}$ and $2^{41.5}$ respectively. In addition, benefiting from the partial calculation, we can attack 33 and 34 (out of 80) steps of RIPEMD-160 with time complexity $2^{67.1}$ and $2^{74.3}$ respectively. When applying the SLDR framework to the differential characteristic used in the Asiacrypt 2017 paper, we significantly improve the time complexity by a factor of $2^{13}$. However, it still cannot compete with the results obtained from the DLSR framework. To the best of our knowledge, these are the best collision attacks on reduced RIPEMD-160 with respect to the number of steps, including the first colliding message pairs for 30 and 31 steps of RIPEMD-160.
##### BibTeX
@article{crypto-2019-29884,
title={Efficient Collision Attack Frameworks for RIPEMD-160},
booktitle={Advances in Cryptology – CRYPTO 2019},
series={Lecture Notes in Computer Science},
publisher={Springer},
volume={11693},
pages={117-149},
doi={10.1007/978-3-030-26951-7_5},
author={<NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME>},
year=2019
}


text:
Hide

# Shortest Missing Subsequences

Given a string $s$ we say that string $t$ is a Subsequence of $s$ if $t$ can be obtained from $s$ by deleting zero or more characters of $s$. Note that $t$ is not necessarily a substring of $s$—that is, $t$ is not necessarily contiguous in $s$, but the characters of $t$ appear in the same order as they do in $s$.

For a given subset, $v$, of the lowercase English alphabet characters from ‘a’ to ‘z’, we say that string $u$ is a Missing Subsequence of another string $s$ if $u$ is not a Subsequence of $s$, but all characters in $u$ and all the characters of $s$ are in the set $v$. A Shortest Missing Subsequence of $s$ is a Missing Subsequence of $s$ with the smallest length among all Missing Subsequences of $s$.

Given a set of English alphabetic characters, a target string made up of characters from that set, and a list of query strings made up of characters from that set, determine if each of the query strings is a Shortest Missing Subsequence of the target string.

## Input

The first line of input contains a string $v$ ($1 \le |v| \le 26$) of lowercase letters, in lexicographical order. Each letter appears at most once. This is the set of alphabetic characters.

The next line of input contains a string $s$ ($1 \le |s| \le 10^6$, $s$ only contains letters from $v$). This is the target string to be queried.

The next line contains an integer $n$ ($1 \le n \le 10^6$). This is the number of queries.

Each of the next $n$ lines contains a string $q$ ($1 \le |q| \le 10^6$, $q$ only contains letters from $v$). These are the query strings. The sum of the lengths of all query strings will not exceed $10^6$.

## Output

Output $n$ lines, one for each query. On each line, output either $1$ if the query string is a Shortest Missing Subsequence of the target string, or $0$ if it is not. The outputs must be in the order of the input queries.

Sample Input 1 Sample Output 1
abc
abcccabac
3
cbb
cbba
cba

1
0
0

CPU Time limit 8 seconds
Memory limit 2048 MB
Difficulty 5.4medium
Statistics Show


text:
Quotient identities

The quotient relation of two trigonometric functions with another trigonometric function is called quotient trigonometric identity, simply called as quotient identity.

Introduction

One trigonometric function can be divided by another trigonometric function mathematically but the quotient form them can also be a trigonometric function in some cases in trigonometry. The quotient relations of trigonometric functions are known as quotient identities and used as formulas in mathematics.

There are two quotient trigonometric identities in trigonometry mathematics.

Sine and Cosine with Tangent

The quotient of sine function by cosine function at an angle is equal to tangent function at the same angle.

$\dfrac{\sin{\theta}}{\cos{\theta}} \,=\, \tan{\theta}$

Cosine and Sine with Cotangent

The quotient of cosine function by sine function at an angle is equal to cotangent function at the same angle.

$\dfrac{\cos{\theta}}{\sin{\theta}} \,=\, \cot{\theta}$

Latest Math Topics
Email subscription
Math Doubts is a best place to learn mathematics and from basics to advanced scientific level for students, teachers and researchers. Know more


text:
# <NAME>

Affiliations:
• University of Wisconsin, Computer Sciences Department
• California Institute of Technology, Center for the Mathematics of Information
• Massachusetts Institute of Technology, Media Laboratory

According to our database1, <NAME> authored at least 152 papers between 2002 and 2022.

Collaborative distances:
• Dijkstra number2 of three.
• Erdős number3 of two.

Book
In proceedings
Article
PhD thesis
Other

## Bibliography

2022
Active Learning for Nonlinear System Identification with Guarantees.
J. Mach. Learn. Res., 2022

Towards Psychologically-Grounded Dynamic Preference Models.
Proceedings of the RecSys '22: Sixteenth ACM Conference on Recommender Systems, Seattle, WA, USA, September 18, 2022

Plenoxels: Radiance Fields without Neural Networks.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021
A Successive-Elimination Approach to Adaptive Robotic Source Seeking.
IEEE Trans. Robotics, 2021

A Lyapunov Analysis of Accelerated Methods in Optimization.
<NAME>. Learn. Res., 2021

Patterns, predictions, and actions: A story about machine learning.
CoRR, 2021

Interpolating Classifiers Make Few Mistakes.
CoRR, 2021

Understanding deep learning (still) requires rethinking generalization.
Commun. ACM, 2021

Preface.
Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

Certainty Equivalent Perception-Based Control.
Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data.
Proceedings of the 38th International Conference on Machine Learning, 2021

Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability.
Proceedings of the 38th International Conference on Machine Learning, 2021

Do Image Classifiers Generalize Across Time?
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty.
Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

2020
On the Sample Complexity of the Linear Quadratic Regulator.
Found. Comput. Math., 2020

Do Offline Metrics Predict Online Performance in Recommender Systems?
CoRR, 2020

A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery.
CoRR, 2020

Finding Equilibrium in Multi-Agent Games with Payoff Uncertainty.
CoRR, 2020

Measuring Robustness to Natural Distribution Shifts in Image Classification.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A System for Massively Parallel Hyperparameter Tuning.
Proceedings of Machine Learning and Systems 2020, 2020

Robust Guarantees for Perception-Based Control.
Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020

Evaluating Machine Accuracy on ImageNet.
Proceedings of the 37th International Conference on Machine Learning, 2020

Neural Kernels Without Tangents.
Proceedings of the 37th International Conference on Machine Learning, 2020

The Effect of Natural Distribution Shift on Question Answering Models.
Proceedings of the 37th International Conference on Machine Learning, 2020

Recommendations and user agency: the reachability of collaboratively-filtered information.
Proceedings of the FAT* '20: Conference on Fairness, 2020

Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions.
Proceedings of the 4th Conference on Robot Learning, 2020

Serverless linear algebra.
Proceedings of the SoCC '20: ACM Symposium on Cloud Computing, 2020

Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
First-order methods almost always avoid strict saddle points.
Math. Program., 2019

A systematic framework for natural perturbations from videos.
CoRR, 2019

Certainty Equivalent Control of LQR is Efficient.
CoRR, 2019

A Meta-Analysis of Overfitting in Machine Learning.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Certainty Equivalence is Efficient for Linear Quadratic Control.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Model Similarity Mitigates Test Set Overuse.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Do ImageNet Classifiers Generalize to ImageNet?
Proceedings of the 36th International Conference on Machine Learning, 2019

The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpoint.
Proceedings of the Conference on Learning Theory, 2019

Learning Linear Dynamical Systems with Semi-Parametric Least Squares.
Proceedings of the Conference on Learning Theory, 2019

Minimax Lower Bounds for H∞-Norm Estimation.
Proceedings of the 2019 American Control Conference, 2019

Mathematical Models of Physiological Responses to Exercise.
Proceedings of the 2019 American Control Conference, 2019

Safely Learning to Control the Constrained Linear Quadratic Regulator.
Proceedings of the 2019 American Control Conference, 2019

2018
Sharp Time-Data Tradeoffs for Linear Inverse Problems.
IEEE Trans. Inf. Theory, 2018

Gradient Descent Learns Linear Dynamical Systems.
<NAME>. <NAME>., 2018

numpywren: serverless linear algebra.
CoRR, 2018

Massively Parallel Hyperparameter Tuning.
CoRR, 2018

Minimax Lower Bounds for ℋ∞-Norm Estimation.
CoRR, 2018

A Successive-Elimination Approach to Adaptive Robotic Sensing.
CoRR, 2018

A Tour of Reinforcement Learning: The View from Continuous Control.
CoRR, 2018

Do CIFAR-10 Classifiers Generalize to CIFAR-10?
CoRR, 2018

Simple random search provides a competitive approach to reinforcement learning.
CoRR, 2018

Tight query complexity lower bounds for PCA via finite sample deformed wigner law.
Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing, 2018

Simple random search of static linear policies is competitive for reinforcement learning.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator.
Proceedings of the 35th International Conference on Machine Learning, 2018

Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification.
Proceedings of the Conference On Learning Theory, 2018

Finite-Data Performance Guarantees for the Output-Feedback Control of an Unknown System.
Proceedings of the 57th IEEE Conference on Decision and Control, 2018

On the Approximation of Toeplitz Operators for Nonparametric H∞-norm Estimation.
Proceedings of the 2018 Annual American Control Conference, 2018

2017
Perturbed Iterate Analysis for Asynchronous Stochastic Optimization.
SIAM J. Optim., 2017

The Alternating Descent Conditional Gradient Method for Sparse Inverse Problems.
SIAM J. Optim., 2017

Saturating Splines and Feature Selection.
<NAME>. <NAME>., 2017

An example of how false conclusions could be made with personalized health tracking and suggestions for avoiding similar situations.
CoRR, 2017

Ground Control to Major Tom: the importance of field surveys in remotely sensed data analysis.
CoRR, 2017

First-order Methods Almost Always Avoid Saddle Points.
CoRR, 2017

On the Approximation of Toeplitz Operators for Nonparametric $\mathcal{H}_\infty$-norm Estimation.
CoRR, 2017

Flare Prediction Using Photospheric and Coronal Image Data.
CoRR, 2017

Non-Asymptotic Analysis of Robust Control from Coarse-Grained Identification.
CoRR, 2017

On the Gap Between Strict-Saddles and True Convexity: An Omega(log d) Lower Bound for Eigenvector Approximation.
CoRR, 2017

Occupy the Cloud: Distributed Computing for the 99%.
CoRR, 2017

Meaningless comparisons lead to false optimism in medical machine learning.
CoRR, 2017

Exponential Stability Analysis via Integral Quadratic Constraints.
CoRR, 2017

Drizzle: Fast and Adaptable Stream Processing at Scale.
Proceedings of the 26th Symposium on Operating Systems Principles, 2017

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Breaking Locality Accelerates Block Gauss-Seidel.
Proceedings of the 34th International Conference on Machine Learning, 2017

Understanding deep learning requires rethinking generalization.
Proceedings of the 5th International Conference on Learning Representations, 2017

KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics.
Proceedings of the 33rd IEEE International Conference on Data Engineering, 2017

A step towards quantifying when an algorithm can and cannot predict an individual's wellbeing.
Proceedings of the Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers, 2017

The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime.
Proceedings of the 30th Conference on Learning Theory, 2017

Occupy the cloud: distributed computing for the 99%.
Proceedings of the 2017 Symposium on Cloud Computing, SoCC 2017, Santa Clara, CA, USA, 2017

2016
Analysis and Design of Optimization Algorithms via Integral Quadratic Constraints.
SIAM J. Optim., 2016

A Lyapunov Analysis of Momentum Methods in Optimization.
CoRR, 2016

Large Scale Kernel Learning using Block Coordinate Descent.
CoRR, 2016

CoRR, 2016

Universality of Mallows' and degeneracy of Kendall's kernels for rankings.
CoRR, 2016

CoRR, 2016

On the Detection of Mixture Distributions with applications to the Most Biased Coin Problem.
CoRR, 2016

Detecting change in depressive symptoms from daily wellbeing questions, personality, and activity.
Proceedings of the 2016 IEEE Wireless Health, 2016

Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics.
Proceedings of the 13th USENIX Symposium on Networked Systems Design and Implementation, 2016

The Power of Adaptivity in Identifying Statistical Alternatives.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Low-rank Solutions of Linear Matrix Equations via Procrustes Flow.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Train faster, generalize better: Stability of stochastic gradient descent.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Best-of-K-bandits.
Proceedings of the 29th Conference on Learning Theory, 2016

Gradient Descent Only Converges to Minimizers.
Proceedings of the 29th Conference on Learning Theory, 2016

2015
Near Minimax Line Spectral Estimation.
IEEE Trans. Inf. Theory, 2015

The Randomized Causation Coefficient.
<NAME>. <NAME>., 2015

Isometric sketching of any set via the Restricted Isometry Property.
CoRR, 2015

Near-Optimal Bounds for Binary Embeddings of Arbitrary Sets.
CoRR, 2015

Parallel Correlation Clustering on Big Graphs.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

A General Analysis of the Convergence of ADMM.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Exponential convergence bounds using integral quadratic constraints.
Proceedings of the 54th IEEE Conference on Decision and Control, 2015

Superresolution without separation.
Proceedings of the 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2015

2014
Blind Deconvolution Using Convex Programming.
IEEE Trans. Inf. Theory, 2014

Fast Methods for Denoising Matrix Completion Formulations, with Applications to Robust Seismic Data Interpolation.
SIAM J. Sci. Comput., 2014

Compressive classification and the rare eclipse problem.
CoRR, 2014

Robust line spectral estimation.
Proceedings of the 48th Asilomar Conference on Signals, Systems and Computers, 2014

2013
Atomic Norm Denoising With Applications to Line Spectral Estimation.
IEEE Trans. Signal Process., 2013

Compressed Sensing Off the Grid.
IEEE Trans. Inf. Theory, 2013

Decomposition Methods for Large Scale LP Decoding.
IEEE Trans. Inf. Theory, 2013

Parallel stochastic gradient algorithms for large-scale matrix completion.
Math. Program. Comput., 2013

Simple bounds for recovering low-complexity models.
Math. Program., 2013

An SVD-free Pareto curve approach to rank minimization
CoRR, 2013

Sparse recovery over continuous dictionaries-just discretize.
Proceedings of the 2013 Asilomar Conference on Signals, 2013

2012
Toward a Noncommutative Arithmetic-geometric Mean Inequality: Conjectures, Case-studies, and Consequences.
Proceedings of the COLT 2012, 2012

Universal Measurement Bounds for Structured Sparse Signal Recovery.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

The Convex Geometry of Linear Inverse Problems.
Found. Comput. Math., 2012

Security Analysis of Smartphone Point-of-Sale Systems.
Proceedings of the 6th USENIX Workshop on Offensive Technologies, 2012

K-subspaces with missing data.
Proceedings of the IEEE Statistical Signal Processing Workshop, 2012

Towards a unified architecture for in-RDBMS analytics.
Proceedings of the ACM SIGMOD International Conference on Management of Data, 2012

Factoring nonnegative matrices with linear programs.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Query Complexity of Derivative-Free Optimization.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Suppressing pseudocodewords by penalizing the objective of LP decoding.
Proceedings of the 2012 IEEE Information Theory Workshop, 2012

Linear system identification via atomic norm regularization.
Proceedings of the 51th IEEE Conference on Decision and Control, 2012

Compressive sensing off the grid.
Proceedings of the 50th Annual Allerton Conference on Communication, 2012

The l1 penalized decoder and its reweighted LP.
Proceedings of the 50th Annual Allerton Conference on Communication, 2012

2011
Null space conditions and thresholds for rank minimization.
Math. Program., 2011

A Simpler Approach to Matrix Completion.
<NAME>. Learn. Res., 2011

Probability of unique integer solution to a system of linear equations.
E<NAME>. Oper. Res., 2011

Simple Bounds for Low-complexity Model Reconstruction
CoRR, 2011

Tight Measurement Bounds for Exact Recovery of Structured Sparse Signals.
CoRR, 2011

Dimensionality reduction: Beyond the Johnson-Lindenstrauss bound.
Proceedings of the Twenty-Second Annual ACM-SIAM Symposium on Discrete Algorithms, 2011

Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

A Framework for Incorporating General Domain Knowledge into Latent Dirichlet Allocation Using First-Order Logic.
Proceedings of the IJCAI 2011, 2011

Atomic norm denoising with applications to line spectral estimation.
Proceedings of the 49th Annual Allerton Conference on Communication, 2011

2010
Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization.
SIAM Rev., 2010

Online Identification and Tracking of Subspaces from Highly Incomplete Information
CoRR, 2010

Practical Large-Scale Optimization for Max-norm Regularization.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Transduction with Matrix Completion: Three Birds with One Stone.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Sample complexity for 1-bit compressed sensing and sparse classification.
Proceedings of the IEEE International Symposium on Information Theory, 2010

High-dimensional Matched Subspace Detection when data are missing.
Proceedings of the IEEE International Symposium on Information Theory, 2010

2009
Exact Matrix Completion via Convex Optimization.
Found. Comput. Math., 2009

Learning kernels from indefinite similarities.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008
Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Necessary and sufficient conditions for success of the nuclear norm heuristic for rank minimization.
Proceedings of the 47th IEEE Conference on Decision and Control, 2008

Determining interconnections in biochemical networks using linear programming.
Proceedings of the 47th IEEE Conference on Decision and Control, 2008

2007
Learning to Transform Time Series with a Few Examples.
IEEE Trans. Pattern <NAME>., 2007

Random Features for Large-Scale Kernel Machines.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Determining Interconnections in Chemical Reaction Networks.
Proceedings of the American Control Conference, 2007

2006
Unsupervised Regression with Applications to Nonlinear System Identification.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Interaction techniques for musical performance with tabletop tangible interfaces.
Proceedings of the International Conference on Advances in Computer Entertainment Technology, 2006

2005
Learning Appearance Manifolds from Video.
Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), 2005

2004
Distributed control of systems over discrete Groups.
IEEE Trans. Autom. Control., 2004

2003
Exploiting symmetry for the distributed control of spatially interconnected systems.
Proceedings of the 42nd IEEE Conference on Decision and Control, 2003

2002
Audiopad: A Tag-based Interface for Musical Performance.
Proceedings of the New Interfaces for Musical Expression, 2002


text:
The Analysis of Gauge-Theoretic Moduli Spaces (17w5149)

Arriving in Banff, Alberta Sunday, August 27 and departing Friday September 1, 2017

Organizers

(University of Arizona)

(University College London)

(Stanford University)

Objectives

There are a large number of basic questions in this area:

a) Moduli spaces arising in gauge-theoretic problems carry $L^2$- (or Weil-Petersson type) metrics which in many cases have interesting special structure. For example, they may be Kaehler or even hyperKaehler. We obtain, in this way, remarkable families of complete hyperKaehler (and hence Ricci-flat) metrics. The asymptotic structure of these moduli space metrics seems to fall into some generalization of the ALE/ALF/ALG/ALH classes that are well-known in the four-dimensional setting in the context of gravitational instantons. In fact, these higher dimensional moduli spaces appear to typically have the structure of a singular torus fibration over a quasi-asymptotically conic space with a generalized `fiber-boundary’ type metric. Proving that this is really the case, not only in special cases but perhaps as a general structural feature, is a guiding question. As a special subcase of this, we mention the moduli space of monopoles on $R^3$, studied initially by Taubes, Atiyah, Gibbons-Manton and many others. Dramatic new progress in understanding its metric structure at infinity, phrased in terms of a compactification as a manifold with corners, is the subject of intense ongoing work.

b) One application coming from the understanding of these metric asymptotics is that it then becomes possible to study the space of $L^2$ harmonic forms, which has direct physical meaning. For the monopole moduli spaces, these spaces of harmonic forms are the subject of the famous Sen conjectures. While no mathematical progress has been made on this question for the past decade, new techniques from geometric microlocal analysis and elsewhere have now matured to the point where these types of problems may soon become tractable. The quantum field theory and string theory interpretation of these moduli spaces suggests predictions for the dimensions of the spaces of their $L^2$ harmonic forms. The mathematical theory should lead to an interpretation of these dimensions in terms of intersection cohomology groups of certain compactifications of these moduli spaces. The key problems involve first establishing the necessary mathematical theory, and then verifying these predictions.

c) Since the work of Seiberg and Witten, the intriguing isometry between the moduli spaces of classical gauge theory solitons, such as monopoles or instantons, and moduli spaces of vacua of quantum gauge theories was intensely studied in physics. For example, moduli spaces of singular monopoles are isometric to Coulomb branches of supersymmetric Quantum Chromodynamics. Currently, a mathematical framework for understanding this relation between quantum and classical moduli spaces is just beginning to emerge. It is a propitious time to develop a much better understanding of this promising relation.

d) The theory of quiver varieties leads to many examples of complete hyperKaehler metrics with interesting asymptotics. The newer and far-reaching theory of bow moduli spaces provides a vast generalization which leads to an understanding of the moduli space of instantons over a 4-dimensional ALF space. These descriptions are very algebraic in a sense, and it is a high priority to understand them in more specifically geometric analytic terms.

e) The Kapustin-Witten equations on a four-manifold with boundary, where the boundary contains a knot, are conjectured to provide a new gauge-theoretic interpretation of the Jones polynomial of that knot; the Haydys-Witten theory in one higher dimension should lead to a similar interpretation of the Khovanov homology. These are conjectured to be related to G\_2 monopoles in seven dimensions. The development of the necessary analytic theory is still in its infancy, and it is expected that these new gauge theories will lead to very interesting new moduli spaces and interesting topological invariants.


text:
n’th derivative of a determinant

Let $A=(a_{i,j})$ be a $d\times d$ matrix whose entries are real functions of $t$. Then,

 $\begin{split}\displaystyle\frac{d^{n}}{dt^{n}}\det(A)&\displaystyle=\sum_{n_{1% }+\cdots+n_{d}=n}{n\choose n_{1},n_{2},...,n_{d}}\sum_{\pi\in S_{d}}% \operatorname{\mathrm{sgn}}(\pi)\prod_{i=1}^{d}\frac{d^{n_{i}}}{dt^{n_{i}}}a_{% i,\pi(i)}\\ \\ &\displaystyle=\sum_{n_{1}+\cdots+n_{d}=n}{n\choose n_{1},n_{2},...,n_{d}}\det% \begin{pmatrix}\frac{d^{n_{1}}}{dt^{n_{1}}}a_{1,1}&\frac{d^{n_{1}}}{dt^{n_{1}}% }a_{1,2}&\cdots&\frac{d^{n_{1}}}{dt^{n_{1}}}a_{1,d}\cr\vdots&\vdots&&\vdots\cr% \frac{d^{n_{d}}}{dt^{n_{d}}}a_{d,1}&\frac{d^{n_{d}}}{dt^{n_{d}}}a_{d,2}&\cdots% &\frac{d^{n_{d}}}{dt^{n_{d}}}a_{d,d}\end{pmatrix}\end{split}$

where ${n\choose n_{1},n_{2},...,n_{r}}$ is the multinomial coefficient, $S_{d}$ is the symmetric group of permutations and $\operatorname{\mathrm{sgn}}(\pi)$ is the sign of a permutation $\pi$.

Title n’th derivative of a determinant NthDerivativeOfADeterminant 2013-03-22 14:30:25 2013-03-22 14:30:25 GeraW (6138) GeraW (6138) 5 GeraW (6138) Result msc 15A15 GeneralizedLeibnizRule MultinomialTheorem DerivativeOfMatrix


text:
emacs-orgmode
[Top][All Lists]

## Re: [O] Way to replace normal tabular env with booktabs?

 From: <NAME> Subject: Re: [O] Way to replace normal tabular env with booktabs? Date: Fri, 13 Jan 2012 09:48:40 -0600

On Fri, Jan 13, 2012 at 9:39 AM, <NAME> wrote:

On Jan 13, 2012, at 3:52 PM, <NAME> wrote:

> There is a patch from me waiting to be incorporated into org mode that lets one use booktabs as export for normal org tables.
>
> You can find it @ http://patchwork.newartisans.com/patch/1016/

Hi NIels,

I am looking now at this patch, and maybe it would be better to implement these three variables as one, holding a property or association list?  Makes it easily extendable.

From a pretty high-level user perspective (one who couldn't have written that patch), I would say that from a usability point of view something like:

,---
| org-export-latex-tables-format
`---

With values of "plain/standard/default" or "booktabs" would be awesome.

That is, unless there's situations where someone would want some combination of regular \hlines mixed with booktabs top and bottom rules?

On a usage note, I was surprised that the patch causes automatic insertion of top and bottom rules even when the org table doesn't use ascii top/bottom rules. In fact, if you use "|-" to put ascii rules on an org chart using this patch, you get double rules.

Just some input from playing with the package a bit.

Thanks again,
John

Equally important - it would be great if you could try to implement this same change in the new exporter engine from Nicolas, to ensure that the new exporter will not lag behind.

Regards

- Carsten

>
>
>
> On Fri, Jan 13, 2012 at 2:21 PM, <NAME> <address@hidden> wrote:
> On Thu, Jan 12, 2012 at 6:01 PM, <NAME> <address@hidden> wrote:
> Hi John,
>
> The Library of Babel comes with your Org-mode distribution.
>
> You'll find it at /contrib/babel/library-of-babel.org
>
>
> I guess you learn something new every day!
>
> In the org file, look for
> * Tables
> ** LaTeX Table Export
>
> There should be functions booktabs and booktabs-notes.
>
> One way to use booktabs is described here:
>
> http://orgmode.org/worg/org-tutorials/org-latex-export.html#sec-13-2
>
>
> I'll check these out. Looked at the worg howto. Not a huge fan of the method, especially with hiding my tables, but I'll give it a shot. I figure there's got to be a simpler way; just change the first \hline -> \toprule and the bottom one to \bottomrule; \midrules in between.
>
> Thanks again,
> John
>
> hth,
> Tom
>
>
> > On Thu, Jan 12, 2012 at 2:12 PM, <NAME> <address@hidden> wrote:
> >
> >> Hi John,
> >>
> >> Agreed, booktabs makes good looking tables.
> >>
> >> Check out your Library of Babel.  There should  be a couple of functions
> >> there that will help you go from Org mode to booktabs.
> >>
> >>
> > Haven't done much with babel other than writing code blocks. Do you mean
> > --- http://orgmode.org/worg/org-contrib/babel/library-of-babel.html
> >
> > Thanks for the suggestion. Feeling a bit lost, but am happy to look around
> > for something that seems similar. I have no elisp-fu, so it'll need to be
> > pretty darn similar :)
> >
> >
> > John
> >
> >
> >> hth,
> >> Tom
> >>
> >> <NAME> <address@hidden> writes:
> >>
> >> > Greetings,
> >> >
> >> >
> >> > I was using wikibooks for some formatting assistance on tables the other
> >> > day and ran into mention of the booktabs package in the "Professional
> >> > tables" section. [1] [2]
> >> >
> >> > I really, really liked it's formatting, especially since one of my column
> >> > headers was a fraction. The standard tabular package places the \hlines
> >> > extremely close to the top and bottom of my header row vs., as the
> >> booktabs
> >> > package says, having extremely nice looking spacing for the table. I
> >> ended
> >> > up doing the table manually inside #+begin_latex block.
> >> >
> >> > Would there be any way to specify that booktabs should be used? The
> >> > formatting is literally identical except for 1) including the booktabs
> >> > package and 2) using \toprule, \midrule and \bottomrule instead of
> >> \hlines.
> >> > In fact, even with booktabs included, if you use \hlines instead of the
> >> > booktab specific lines, you'll get a "regular" tabular table.
> >> >
> >> > Any thoughts on this?
> >> >
> >> >
> >> > Best regards,
> >> > John
> >> >
> >> > -----
> >> > [1] http://en.wikibooks.org/wiki/LaTeX/Tables#Professional_tables
> >> > [2] http://www.ctan.org/tex-archive/macros/latex/contrib/booktabs/
> >> > Greetings,I was using wikibooks for some formatting assistance on tables
> >> the other day and ran into mention of the booktabs package in the
> >> "Professional tables" section. [1] [2]
> >> > I really, really liked it's formatting, especially since one of my
> >> column headers was a fraction. The standard tabular package places the
> >> \hlines extremely close to the top and bottom of my header row vs., as the
> >> booktabs package says, having extremely nice looking spacing for the table.
> >> I ended up doing the table manually inside #+begin_latex block.
> >> > Would there be any way to specify that booktabs should be used? The
> >> formatting is literally identical except for 1) including the booktabs
> >> package and 2) using \toprule, \midrule and \bottomrule instead of \hlines.
> >> In fact, even with booktabs included, if you use \hlines instead of the
> >> booktab specific lines, you'll get a "regular" tabular table.
> >> > Any thoughts on this?Best regards,John-----[1]
> >> http://en.wikibooks.org/wiki/LaTeX/Tables#Professional_tables
> >> > [2] http://www.ctan.org/tex-archive/macros/latex/contrib/booktabs/
> >>
> >> --
> >> <NAME>
> >> http://www.tsdye.com
> >>
> > On Thu, Jan 12, 2012 at 2:12 PM, <NAME> <mailto:address@hidden> wrote:
> > Hi John,
> >
> > Agreed, booktabs makes good looking tables.
> >
> > Check out your Library of Babel.  There should  be a couple of functions
> > there that will help you go from Org mode to booktabs.
> > Haven't done much with babel other than writing code blocks. Do you mean this page?--- http://orgmode.org/worg/org-contrib/babel/library-of-babel.html
> > Thanks for the suggestion. Feeling a bit lost, but am happy to look around for something that seems similar. I have no elisp-fu, so it'll need to be pretty darn similar :)
> > John
> > hth,
> > Tom
> >
> > <NAME> <mailto:address@hidden> writes:
> >
> >> Greetings,
> >>
> >>
> >> I was using wikibooks for some formatting assistance on tables the other
> >> day and ran into mention of the booktabs package in the "Professional
> >> tables" section. [1] [2]
> >>
> >> I really, really liked it's formatting, especially since one of my column
> >> headers was a fraction. The standard tabular package places the \hlines
> >> extremely close to the top and bottom of my header row vs., as the booktabs
> >> package says, having extremely nice looking spacing for the table. I ended
> >> up doing the table manually inside #+begin_latex block.
> >>
> >> Would there be any way to specify that booktabs should be used? The
> >> formatting is literally identical except for 1) including the booktabs
> >> package and 2) using \toprule, \midrule and \bottomrule instead of \hlines.
> >> In fact, even with booktabs included, if you use \hlines instead of the
> >> booktab specific lines, you'll get a "regular" tabular table.
> >>
> >> Any thoughts on this?
> >>
> >>
> >> Best regards,
> >> John
> >>
> >> -----
> >> [1] http://en.wikibooks.org/wiki/LaTeX/Tables#Professional_tables
> >> [2] http://www.ctan.org/tex-archive/macros/latex/contrib/booktabs/
> >> Greetings,I was using wikibooks for some formatting assistance on tables the other day and ran into mention of the booktabs package in the "Professional tables" section. [1] [2]
> >> I really, really liked it's formatting, especially since one of my column headers was a fraction. The standard tabular package places the \hlines extremely close to the top and bottom of my header row vs., as the booktabs package says, having extremely nice looking spacing for the table. I ended up doing the table manually inside #+begin_latex block.
> >
> >> Would there be any way to specify that booktabs should be used? The formatting is literally identical except for 1) including the booktabs package and 2) using \toprule, \midrule and \bottomrule instead of \hlines. In fact, even with booktabs included, if you use \hlines instead of the booktab specific lines, you'll get a "regular" tabular table.
> >
> >> Any thoughts on this?Best regards,John-----[1] http://en.wikibooks.org/wiki/LaTeX/Tables#Professional_tables
> >> [2] http://www.ctan.org/tex-archive/macros/latex/contrib/booktabs/
> >
> > --
> > <NAME>
> > http://www.tsdye.com
> >
>
> --
> <NAME>
> http://www.tsdye.com
>
>
>
>
> --
> http://pft.github.com

- Carsten


text:
# Circumference of a Circle – Explanation & Examples

We saw before how to find the perimeter of the polygon. We know that circle is not a polygon. Therefore, it should not have a perimeter. We use an equivalent form for a circle, called circumference.

In this article, we will discuss how to find a circle’s circumference, the circumference of a circle formula, examples, and sample problems about the circumference of a circle.

## What is the circumference of a circle?

The distance around a polygon, such as a square or a rectangle, is called the perimeter (P).  On the other hand, the distance around a circle is referred to as the circumference (C). Therefore, the circumference of a circle is the linear distance of an edge of the circle.

### Why do we do need to calculate the circumference of a circle?

Finding the circumference of an object is important in the following scenarios:

Whether you want to buy a bra, trouser, or sweater, you need to know the distance around your waist or chest. Though your body isn’t a perfect circle, you will have to measure its circumference using a tape measure. Tailors mostly use this technique to determine the circumference of a dress.

You also need to know the circumference of a circle doing craftwork, putting fencing around your hot tub, or just solving a math problem for school.

## How to find the circumference of a circle?

As stated before, the perimeter or circumference of a circle is the distance around a circle or any circular shape. The circumference of a circle is the same as the length of a straight line folded or bent to make the circle. The circumference of a circle is measured in meters, kilometers, yards, inches, etc.

There are two ways of finding the perimeter or circumference of a circle. The first formula involves using the radius, and the second involves using the diameter of a circle. It is important to note that both two methods yield the same result.

Let’s take a look.

The circumference of a circle is given by;

C = 2 * π* R = 2πR

where,

C = Circumference or perimeter,

R = the radius of a circle,

π = the mathematical constant known as Pi

Or

C = π* D = π D

where, D = 2R = The diameter of a circle

For any circle, its circumference ratio to its diameter is equal to a constant known as pi.

Circumference/Diameter = Pi

C /D = Pi or C/2R = pi

The approximate value of pi (π) = 22/7 = 3.1415926535897…. (a non-terminating value)

For the easier computation of a circle’s circumference, pi’s value is taken to be 3.14 (π = 3.14).

Let’s see a few examples below to polish the concept of the circumference.

Example 1

Find the circumference of the circle with a radius of 8 cm.

Solution

Circumference = 2 * π* R = 2πR

= 2 * 3.14 * 8

= 50.24 cm.

Example 2

Calculate the circumference of a circle whose diameter is 70 mm

Solution

Circumference = π* D = π D

= 3.14 * 70

= 219.8 mm

Example 3

Calculate the perimeter of a circular flower garden whose radius is 10 m.

Solution

Circumference = 2 * π* R = 2πR

= 2 * 3.14 * 10

= 62.8 m.

Example 4

The circumference of a circle is 440 yards. Find the diameter and radius of the circle.

Solution

Circumference = 2 * π* R = 2πR

440 =2 * 3.14 * R

440 = 6.28R

Divide both sides by 6.28 to get,

R = 70.06

Therefore, the radius of the circle is 70.06 yards. But, since the diameter is twice the radius of a circle, the diameter is equal to 140.12 yards.

Example 5

The diameter of the wheels of a bicycle is 100 cm. How many revolutions will each wheel make to travel a distance of 157 meters?

Solution

Calculate the circumference of the bicycle’s wheel.

Circumference = π D

= 3.14 * 100

= 314 cm

To get the number of revolutions of the wheel, divide the distance covered by the circumference of the wheel.

We need to convert 157 meters to cm before dividing, so we multiply 157 by 100 to get 15700 cm. Therefore,

Number of revolutions = 15700 cm/314 cm

= 50 revolutions.

Example 6

A piece of a wire in the form of a rectangle of length 100 cm and width 50 cm is cut and folded to make a circle. Calculate the circumference and radius of the circle formed.

Solution

The circumference of the circle formed = the perimeter of the rectangular wire.

Perimeter of a rectangle = 2(L + W)

= 2(100 + 50) cm

= 2 * 150 cm

= 300 cm.

Therefore, the circumference of the circle will be 300 cm.

Circumference = 2 π R

300 cm = 2 * π * R

300 cm = 2 * 3.14 * R

300 cm = 6.28R

Divide both sides by 6.28.

R = 47.77 cm

So, the radius of the circle will be 47.77 cm.

Example 7

The radius of each wheel of a motorcycle is 0. 85 m. How far will the motorcycle move if each wheel takes 1000 revolutions? Assume the motorcycle is moving on a straight line.

Solution

First, find the circumference of the wheel.

Circumference = 2 π R

= 2 * 3.14 * 0.85

= 5.338 m.

To find the distance traveled, multiply the circumference of the wheel by the number of revolutions taken.

Distance = 5.338 * 1000

= 5338 m

Therefore, the distance traveled is equal to 5.338 kilometers.

### Practice Questions

1. What is the circumference of the circle with a radius of $12$ ft? Use $\pi \approx 3.14$.

2. What is the circumference of a circle whose diameter is $168$ m? Use $\pi = \dfrac{22}{7}$.

3. Suppose that the circumference of a circle is $770$ yards. What is its diameter?

4. A $12$-inch pizza is served to Mike and his friends. Mike is interested in calculating its circumference. If Mike approximates $\pi$ as $3.14$, what would be the circumference of the pizza?

5. The perimeter of a particular square is one-third the perimeter of a particular circle. If the length of the square is $L$ units, what is the circle’s diameter in terms of $L$?


text:
# How to reduce the number of feature levels without losing information?

Some of the features I am working with has more than 300 factor levels. I tried to reduce their number of levels with a 'fake dummy' method. For example, I replaced one 600-level predictor with 4 predictors (as $600<5^4$). Is this a valid approach?

-
 Could you explain how these 4 predictors were coded? – chl♦ Sep 24 '12 at 17:54 when use 0/1 coded factor,you need 10 (600<2^10) predictors,I set every predictor have no more than 5 levels. – <NAME> Sep 24 '12 at 18:16 I'm not sure to follow, because it is my understanding that a $k$-level factor is represented as $k-1$ dummy variables in a design matrix. Could you give us a little more details? Or do you just mean that you merged several factor levels together so that in the end you got 10 dummies instead of 600 levels (which would require 599 dummies), for a single predictor? – chl♦ Sep 24 '12 at 21:02 when transform to dummy variable,only one variable are 1 others are zero,i want to reduce the number of predictors,so more than one variables can be 1 – Chenghao Liu Sep 25 '12 at 3:49


text:
Volume 340 - The 39th International Conference on High Energy Physics (ICHEP2018) - Parallel: Strong Interactions and Hadron Physics
Hyperon studies at BESIII
<NAME>* On behalf of the BESIII collaboration
*corresponding author
Full text: pdf
Published on: August 02, 2019
Abstract
Recent results on $e^+e^-\to\Lambda\bar\Lambda$ from BESIII Collaboration are reported: ($i$) for the process at $J/\psi$ and ($ii$) for the non-resonant production.
($i$) Using a data sample of $1.31\times 10^9$ $J/\psi$ events we report the first observation of spin polarization of $\Lambda$ and $\bar\Lambda$ hyperons. The phase between the helicity flip and helicity non-flip amplitudes of $\Delta\Phi=(42.4\pm0.6\pm0.5)^\circ$ is determined. The decay asymmetries for $\Lambda\to p\pi^-$ ($\alpha_-$), $\bar\Lambda\to\bar p\pi^+$ ($\alpha_+$) and $\bar\Lambda\to\bar n\pi^0$ ($\bar\alpha_0$) are measured to be $\alpha_-=0.750\pm0.009\pm0.004$, $\alpha_+=-0.758\pm0.010\pm0.007$ and $\bar\alpha_0=-0.692\pm0.016\pm0.006$, respectively. The obtained value of $\alpha_-$ is higher by $(17\pm3)\%$ than the world average of $0.642\pm0.013$ used for all $\Lambda$ polarization measurements since 1978. The $CP$ asymmetry $A_{CP}=(\alpha_-+\alpha_+)/(\alpha_--\alpha_+)$ of $-0.006\pm0.012\pm0.007$ and the ratio $\bar{\alpha}_{0}/\alpha_{+} = 0.913\pm 0.028 \pm 0.012$ are also measured.
($ii$) Using 66.9 pb$^{-1}$ collected at $\sqrt{s} = 2.396$ GeV $\Lambda$ form factors are determined. This is the first complete determination of the time-like elastic form factors $G_M$ and $G_E$ for any baryon: the ratio $R = |G_E/G_M|$ and the relative phase $\Delta\Phi=\arg(G_E/G_M)$. Using the decay asymmetry parameters from analysis ($i$) as input, the obtained values are $R = 0.96\pm 0.14\pm 0.02$ and $\Delta\Phi = (37\pm 12\pm 6)^\circ$, respectively. The normalization of the form factors is set by the determined Born cross section cross section of $\sigma_{Born} = 119.0\pm5.3\pm5.1$ pb.
In a separate analysis a surprisingly large Born cross section of $305\pm45^{+66}_{-36}$ pb is measured at $\sqrt{s}$=2.2324 GeV, which is 1.0 MeV above the threshold.
DOI: https://doi.org/10.22323/1.340.0550
How to cite

Metadata are provided both in "article" format (very similar to INSPIRE) as this helps creating very compact bibliographies which can be beneficial to authors and readers, and in "proceeding" format which is more detailed and complete.

Open Access
Copyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.


text:
# Competitive Musical Chairs!

You play musical chairs with 28 other people. The game start with 17 chairs.

The probability that you will not reach the final 10 rounds is $${\frac{a}{b}}$$.

What is the value of $${a+b}$$?

$$\textbf{Details and Assumptions}$$

In musical chairs, players have to sit down on a chair by the end of the round. If a player isn't able to find a chair to sit down on by the end of the round, they are eliminated from the game. Every round, a chair is removed. The game ends when there is only one chair remaining, and the winner is the player that sits on the final chair. Each player has an equal chance of finding a chair.

The fraction $${\frac{a}{b}}$$ is in its simplest form.

This question is part of the set Musical Chairs. If you liked this question, try some of the other questions as well!

×


text:
# Khan/khan-exercises

Switch branches/tags
Nothing to show
Fetching contributors…
Cannot retrieve contributors at this time
346 lines (307 sloc) 16.4 KB
 Special right triangles
randRange(2, 7)

In the right triangle shown, AC = BC = AC.

What is AB?

betterTriangle( 1, 1, "A", "B", "C", AC, AC, "x" );
AC * AC * 2

We know the length of each leg, and want to find the length of the hypotenuse. What mathematical relationship is there between a right triangle's leg and its hypotenuse?

\qquad AC^2 + BC^2 = x^2

\qquad AC^2 + AC^2 = x^2

\qquad x^2 = 2 \cdot AC^2

\qquad x = \sqrt{2 \cdot AC^2}

\qquad x = \sqrt{2} \cdot \sqrt{AC^2}

2 * randRange(2, 6)

In the right triangle shown, AC = BC and AB = AB.

How long are each of the legs?

betterTriangle( 1, 1, "A", "B", "C", "x", "x", AB );
AB * AB / 2

We know the length of the hypotenuse, and want to find the length of each leg. What mathematical relationship is there between a right triangle's legs and its hypotenuse?

\qquad x^2 + x^2 = AB^2

\qquad 2 \cdot x^2 = AB^2

\qquad x^2 = AB * AB / 2

\qquad x = \sqrt{AB * AB / 2}

\qquad x = \sqrt{AB * AB / 4 \cdot 2}

\qquad x = \sqrt{AB * AB / 4} \cdot \sqrt{2}

2 * randRange(2, 6)

In the right triangle shown, AC = BC and AB = AB\sqrt{2}.

How long are each of the legs?

betterTriangle( 1, 1, "A", "B", "C", "x", "x", AB + "\\sqrt{2}" );
AB * AB

We know the length of the hypotenuse, and want to find the length of each leg. What mathematical relationship is there between a right triangle's legs and its hypotenuse?

\qquad x^2 + x^2 = AB^2

\qquad 2 \cdot x^2 = (AB\sqrt{2})^2

\qquad 2 \cdot x^2 = AB^2 \cdot (\sqrt{2})^2

\qquad 2 \cdot x^2 = AB * AB \cdot 2

\qquad x^2 = AB * AB

randRange(2, 6) randFromArray([[1, ""], [3, "\\sqrt{3}"]]) BC + BCrs rand(2) ["\\angle A = 30^\\circ", "\\angle B = 60^\\circ"][ANGLE]

In the right triangle shown, mAB and BC = BC + BCrs.

How long is AB?

betterTriangle(1, sqrt(3), "A", "B", "C", BC + BCrs, "", "x"); if (ANGLE === 0) { arc([0, 5 * sqrt(3) / 2], 0.8, 270, 300); label([-0.1, (5 * sqrt(3) / 2) - 1], "{30}^{\\circ}", "below right"); } else { arc([5/2,0], 0.5, 120, 180); label([5/2-0.2, 0], "{60}^{\\circ}", "above left"); }
4 * BC * BC * BCr

We know the length of a leg, and want to find the length of the hypotenuse. What is the relationship between the sides of a 30 - 60 - 90 triangle?

This is a 30-60-90 triangle with a hypotenuse of length 1.

betterTriangle(1, sqrt(3), "A", "B", "C", "\\dfrac{1}{2}", "\\dfrac{\\sqrt{3}}{2}", 1); arc([0, 5 * sqrt(3) / 2], 0.8, 270, 300); arc([5/2,0], 0.5, 120, 180); label([-0.1, (5 * sqrt(3) / 2) - 1], "{30}^{\\circ}", "below right"); label([5/2-0.2, 0], "{60}^{\\circ}", "above left");

The ratio of AB : BC is 1 : \dfrac{1}{2}.

Therefore, \dfrac{x}{BC + BCrs} = \dfrac{1}{\frac{1}{2}} = 2.

x = 2 \cdot BC + BCrs

x = BC * 2 + BCrs

3 * randRange( 2, 6 ) randFromArray([ [1, "", (AC * 2 / 3) + "\\sqrt{3}", AC * AC * 4 / 3], [3, "\\sqrt{3}", (AC * 2), AC * AC * 4] ]) AC + ACrs rand(2) ["\\angle A = 30^\\circ", "\\angle B = 60^\\circ"][ANGLE]

In the right triangle shown, mAB and AC = AC + ACrs.

How long is AB?

betterTriangle(1, sqrt(3), "A", "B", "C", "", AC + ACrs, "x"); if (ANGLE === 0) { arc([0, 5 * sqrt(3) / 2], 0.8, 270, 300); label([-0.1, (5 * sqrt(3) / 2) - 1], "{30}^{\\circ}", "below right"); } else { arc([5/2,0], 0.5, 120, 180); label([5/2-0.2, 0], "{60}^{\\circ}", "above left"); }
AB

We know the length of a leg, and want to find the length of the hypotenuse. What is the relationship between the sides of a 30 - 60 - 90 triangle?

This is a 30-60-90 triangle with a hypotenuse of length 1.

betterTriangle(1, sqrt(3), "A", "B", "C", "\\dfrac{1}{2}", "\\dfrac{\\sqrt{3}}{2}", 1); arc([0, 5 * sqrt(3) / 2], 0.8, 270, 300); arc([5 / 2,0], 0.5, 120, 180); label([-0.1, (5 * sqrt(3) / 2) - 1], "{30}^{\\circ}", "below right"); label([5 / 2 - 0.2, 0], "{60}^{\\circ}", "above left");

The ratio of AB : AC is 1 : \dfrac{\sqrt{3}}{2}.

Therefore, \dfrac{x}{AC + ACrs} = \dfrac{1}{\frac{\sqrt{3}}{2}} = \dfrac{2}{\sqrt{3}}.

x = \dfrac{2}{\sqrt{3}} \cdot AC + ACrs

x = \dfrac{2 * AC}{\sqrt{3}}

x = \dfrac{2 * AC}{\sqrt{3}} \cdot \dfrac{\sqrt{3}}{\sqrt{3}}

x = \dfrac{2 * AC \cdot \sqrt{3}}{3}

So, x = ABs.

randRange( 2, 6 ) randFromArray([ [1, ""], [3, "\\sqrt{3}"] ]) 2*BC + BCrs rand(2) ["\\angle A = 30^\\circ", "\\angle B = 60^\\circ"][ANGLE]

In the right triangle shown, mAB and AB = 2 * BC + BCrs.

How long is BC?

betterTriangle(1, sqrt(3), "A", "B", "C", "x", "", 2 * BC + BCrs); if (ANGLE === 0) { arc([0, 5 * sqrt(3) / 2], 0.8, 270, 300); label([-0.1, (5 * sqrt(3) / 2) - 1], "{30}^{\\circ}", "below right"); } else { arc([5/2,0], 0.5, 120, 180); label([5/2-0.2, 0], "{60}^{\\circ}", "above left"); }
BC * BC * BCr

We know the length of the hypotenuse, and want to find the length of the shortest side. What is the relationship between the sides of a 30 - 60 - 90 triangle?

This is a 30-60-90 triangle with a hypotenuse of length 1.

betterTriangle(1, sqrt(3), "A", "B", "C", "\\dfrac{1}{2}", "\\dfrac{\\sqrt{3}}{2}", 1); arc([0, 5 * sqrt(3) / 2], 0.8, 270, 300); arc([5/2,0], 0.5, 120, 180); label([5/2-0.2, 0], "{60}^{\\circ}", "above left"); label([-0.1, (5 * sqrt(3) / 2) - 1], "{30}^{\\circ}", "below right");

The ratio of BC : AB is \dfrac{1}{2} : 1.

Therefore, \dfrac{x}{ABdisp} = \dfrac{1}{2}.

x = BC + BCrs

3 * randRange( 2, 6 ) randFromArray([ [1, "", (AC * 2 / 3) + "\\sqrt{3}", AC * AC * 4 / 3], [3, "\\sqrt{3}", (AC * 2), AC * AC * 4] ]) rand(2) ["\\angle A = 30^\\circ", "\\angle B = 60^\\circ"][ANGLE]

In the right triangle shown, mAB and AB = ABs.

How long is AC?

betterTriangle(1, sqrt(3), "A", "B", "C", "", "x", ABs); if (ANGLE === 0) { arc([0, 5 * sqrt(3) / 2], 0.8, 270, 300); label([-0.1, (5 * sqrt(3) / 2) - 1], "{30}^{\\circ}", "below right"); } else { arc([5/2,0], 0.5, 120, 180); label([5/2-0.2, 0], "{60}^{\\circ}", "above left"); }
AC * AC * ACr

We know the length of the hypotenuse, and want to find the length of the longest side. What is the relationship between the sides of a 30 - 60 - 90 triangle?

This is a 30-60-90 triangle with a hypotenuse of length 1.

betterTriangle(1, sqrt(3), "A", "B", "C", "\\dfrac{1}{2}", "\\dfrac{\\sqrt{3}}{2}", 1); arc([0, 5 * sqrt(3) / 2], 0.8, 270, 300); arc([5 / 2,0], 0.5, 120, 180); label([-0.1, (5 * sqrt(3) / 2) - 1], "{30}^{\\circ}", "below right"); label([5 / 2 - 0.2, 0], "{60}^{\\circ}", "above left");

The ratio of AC : AB is \dfrac{\sqrt{3}}{2} : 1.

Therefore, \dfrac{x}{ABs} = \dfrac{\sqrt{3}}{2}.

x = \dfrac{\sqrt{3}}{2} \cdot ABs

x = AC / 3 \cdot \sqrt{3} \cdot \sqrt{3}

So, x = AC + ACrs.


text: Table 4. Summary of relations among meters {1,0} to{0,3} including B, P, S, O, U, R, Sym, and C. Where multiple relations are present, the most restricted form(s) are given (cf. the structure of entailments in Figure 24). For instance, V does not appear, as all V-related pairs are accounted for by more restricted relations (R, C, and Sym) for these meters. All relations have been restricted to exact equivalences (including P). Notice the diagonal axes of C and Sym relations, as well as the clusters of R blocks (as shown earlier in Tables 1, 2, and 3 at the end of the section on vector identity).