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# Mark Dredze

 Research Scientist Human Language Technology Center of Excellence (HLTCOE) Assistant Research Professor Department of Computer Science Center for Language and Speech Processing (CLSP) Machine Learning Group Center for Population Health Information Technology (CPHIT), Bloomberg Health Sciences Informatics, School of Medicine
 Contact: |  www.cs.jhu.edu/~mdredze   www.dredze.com Office: Stieff 181    (410) 516-6786

## Publications

Click to show abstract.

 2013 (9 Publications) Mark Dredze, Michael J Paul, Shane Bergsma, Hieu Tran. Carmen: A Twitter Geolocation System with Applications to Public Health. AAAI Workshop on Expanding the Boundaries of Health Informatics Using AI (HIAI), 2013. [Code] Michael J Paul, Byron Wallace, Mark Dredze. What Affects Patient (Dis)satisfaction? Analyzing Online Doctor Ratings with a Joint Topic-Sentiment Model. AAAI Workshop on Expanding the Boundaries of Health Informatics Using AI (HIAI), 2013. Justin Snyder, Rebecca Knowles, Mark Dredze, Matthew R. Gormley, Travis Wolfe. Topic Models and Metadata for Visualizing Text Corpora. North American Chapter of the Association for Computational Linguistics (NAACL) (Demo Paper), 2013. Effectively exploring and analyzing large text corpora requires visualizations that provide a high level summary. Past work has relied on faceted browsing of document metadata or on natural language processing of document text. In this paper, we present a new web-based tool that integrates topics learned from an unsupervised topic model in a faceted browsing experience. The user can manage topics, filter documents by topic and summarize views with metadata and topic graphs. We report a user study of the usefulness of topics in our tool. Damianos Karakos, Mark Dredze, Sanjeev Khudanpur. Estimating Confusions in the ASR Channel for Improved Topic-based Language Model Adaptation. Technical Report 8, Johns Hopkins University, 2013. [PDF] Shane Bergsma, Mark Dredze, Benjamin Van Durme, Theresa Wilson, David Yarowsky. Broadly Improving User Classification via Communication-Based Name and Location Clustering on Twitter. North American Chapter of the Association for Computational Linguistics (NAACL), 2013. Mahesh Joshi, Mark Dredze, William W. Cohen, Carolyn P. Rose. What's in a Domain? Multi-Domain Learning for Multi-Attribute Data. North American Chapter of the Association for Computational Linguistics (NAACL), 2013. Alex Lamb, Michael J. Paul, Mark Dredze. Separating Fact from Fear: Tracking Flu Infections on Twitter. North American Chapter of the Association for Computational Linguistics (NAACL), 2013. [PDF] Michael Paul, Mark Dredze. Drug Extraction from the Web: Summarizing Drug Experiences with Multi-Dimensional Topic Models. North American Chapter of the Association for Computational Linguistics (NAACL), 2013. [PDF] Koby Crammer, Alex Kulesza, Mark Dredze. Adaptive Regularization of Weight Vectors. Machine Learning, 2013. [PDF]

 2009 (6 Publications) Mark Dredze. Intelligent Email: Aiding Users with AI. PhD Thesis, Computer and Information Science, University of Pennsylvania, 2009. [PDF] Paul McNamee, Mark Dredze, Adam Gerber, Nikesh Garera, Tim Finin, James Mayfield, Christine Piatko, Delip Rao, David Yarowsky, Markus Dreyer. HLTCOE Approaches to Knowledge Base Population at TAC 2009. Text Analysis Conference (TAC), 2009. Koby Crammer, Alex Kulesza, Mark Dredze. Adaptive Regularization of Weight Vectors. Advances in Neural Information Processing Systems (NIPS), 2009. [PDF] We present AROW, a new online learning algorithm that combines several useful properties: large margin training, confidence weighting, and the capacity to handle non-separable data. AROW performs adaptive regularization of the prediction function upon seeing each new instance, allowing it to perform especially well in the presence of label noise. We derive a mistake bound, similar in form to the second order perceptron bound, that does not assume separability. We also relate our algorithm to recent confidence-weighted online learning techniques and show empirically that AROW achieves state-of-the-art performance and notable robustness in the case of non-separable data. Mark Dredze, Partha Pratim Talukdar, Koby Crammer. Sequence Learning from Data with Multiple Labels. ECML/PKDD Workshop on Learning from Multi-Label Data, 2009. [PDF] We present novel algorithms for learning structured predictors from instances with multiple labels in the presence of noise. The proposed algorithms improve performance on two standard NLP tasks when we have a small amount of training data (low quantity) and when the labels are noisy (low quality). In these settings, the methods improve performance over using a single label, in some cases exceeding performance using gold labels. Our methods could be used in a semi-supervised setting, where a limited amount of labeled data could be combined with a rule based automatic labeling of unlabeled data with multiple possible labels. Koby Crammer, Mark Dredze, Alex Kulesza. Multi-Class Confidence Weighted Algorithms. Empirical Methods in Natural Language Processing (EMNLP), 2009. [PDF] The recently introduced online confidence-weighted (CW) learning algorithm for binary classification performs well on many binary NLP tasks. However, for multi-class problems CW learning updates and inference cannot be computed analytically or solved as convex optimization problems as they are in the binary case. We derive learning algorithms for the multi-class CW setting and provide extensive evaluation using nine NLP datasets, including three derived from the recently released New York Times corpus. Our best algorithm outperforms state-of-the-art online and batch methods on eight of the nine tasks. We also show that the confidence information maintained during learning yields useful probabilistic information at test time. Mark Dredze, Bill Schilit, Peter Norvig. Suggesting Email View Filters for Triage and Search. International Joint Conference on Artificial Intelligence (IJCAI), 2009. [PDF] Growing email volumes cause flooded inboxes and swelled email archives, making search and new email processing difficult. While emails have rich metadata, such as recipients and folders, suitable for creating filtered views, it is often difficult to choose appropriate filters for new inbox messages without first examining messages. In this work, we consider a system that automatically suggests relevant view filters to the user for the currently viewed messages. We propose several ranking algorithms for suggesting useful filters. Our work suggests that such systems quickly filter groups of inbox messages and find messages more easily during search.

 2008 (15 Publications) Kevin Lerman, Ari Gilder, Mark Dredze, Fernando Pereira. Reading the Markets: Forecasting Public Opinion of Political Candidates by News Analysis. Conference on Computational Linguistics (Coling), 2008. [PDF] Media reporting shapes public opinion which can in turn influence events, particularly in political elections, in which candidates both respond to and shape public perception of their campaigns. We use computational linguistics to automatically predict the impact of news on public perception of political candidates. Our system uses daily newspaper articles to predict shifts in public opinion as reflected in prediction markets. We discuss various types of features designed for this problem. The news system improves market prediction over baseline market systems. Mark Dredze, Joel Wallenberg. Further Results and Analysis of Icelandic Part of Speech Tagging. Technical Report MS-CIS-08-13, University of Pennsylvania, Department of Computer and Information Science, 2008. [PDF] Data driven POS tagging has achieved good performance for English, but can still lag behind linguistic rule based taggers for morphologically complex languages, such as Icelandic. We extend a statistical tagger to handle fine grained tagsets and improve over the best Icelandic POS tagger. Additionally, we develop a case tagger for non-local case and gender decisions. An error analysis of our system suggests future directions. This paper presents further results and analysis to the original work. Mark Dredze, Joel Wallenberg. Icelandic Data-Driven Part of Speech Tagging. Association for Computational Linguistics (ACL), 2008. [PDF] Data driven POS tagging has achieved good performance for English, but can still lag behind linguistic rule based taggers for morphologically complex languages, such as Icelandic. We extend a statistical tagger to handle fine grained tagsets and improve over the best Icelandic POS tagger. Additionally, we develop a case tagger for non-local case and gender decisions. An error analysis of our system suggests future directions. Kuzman Ganchev, Mark Dredze. Small Statistical Models by Random Feature Mixing. Workshop on Mobile NLP at ACL, 2008. [PDF] The application of statistical NLP systems to resource constrained devices is limited by the need to maintain parameters for a large number of features and an alphabet mapping features to parameters. We introduce random feature mixing to eliminate alphabet storage and reduce the number of parameters without severely impacting model performance. Koby Crammer, Mark Dredze, John Blitzer, Fernando Pereira. Batch Performance for an Online Price. The NIPS 2007 Workshop on Efficient Machine Learning, 2008. [PDF] Batch learning techniques achieve good performance, but at the cost of many (sometimes even hundreds) of passes over the data. For many tasks, such as web-scale ranking of machine translation hypotheses, making many passes over the data is prohibitively expensive, even in parallel over thousands of machines. Online algorithms, which treat data as a stream of examples, are conceptually appealing for these large scale problems. In practice, however, online algorithms tend to underperform batch methods, unless they are themselves run in multiple passes over the data.

 2006 (4 Publications) Mark Dredze, John Blitzer, Koby Crammer, Fernando Pereira. Feature Design for Transfer Learning. North East Student Colloquium on Artificial Intelligence (NESCAI), 2006. [PDF] Mark Dredze, John Blitzer, Fernando Pereira. Sorry, I Forgot the Attachment:'' Email Attachment Prediction. Conference on Email and Anti-Spam (CEAS), 2006. [PDF] The missing attachment problem: a missing attachment generates a wave of emails from the recipients notifying the sender of the error. We present an attachment prediction system to reduce the volume of missing attachment mail. Our classifier could prompt an alert when an outgoing email is missing an attachment. Additionally, the system could activate an attachment recommendation system, whereby suggested documents are offered once the system determines the user is likely to include an attachment, effectively reminding the user to include the attachment. We present promising initial results and discuss implications of our work. Mark Dredze, Tessa Lau, Nicholas Kushmerick. Automatically classifying emails into activities. Intelligent User Interfaces (IUI), 2006. [PDF] Email-based activity management systems promise to give users better tools for managing increasing volumes of email, by organizing email according to a user\'s activities. Current activity management systems do not automatically classify incoming messages by the activity to which they belong, instead relying on simple heuristics (such as message threads), or asking the user to manually classify incoming messages as belonging to an activity. This paper presents several algorithms for automatically recognizing emails as part of an ongoing activity. Our baseline methods are the use of message reply-to threads to determine activity membership and a naive Bayes classifier. Our SimSubset and SimOverlap algorithms compare the people involved in an activity against the recipients of each incoming message. Our SimContent algorithm uses IRR (a variant of latent semantic indexing) to classify emails into activities using similarity based on message contents. An empirical evaluation shows that each of these methods provide a significant improvement to the baseline methods. In addition, we show that a combined approach that votes the predictions of the individual methods performs better than each individual method alone. Nicholas Kushmerick, Tessa Lau, Mark Dredze, Rinat Khoussainov. Activity-Centric Email: A Machine Learning Approach. American National Conference on Artificial Intelligence (AAAI), 2006. [PDF]

 2004 (1 Publications) Mark Dredze, Jeffrey Stylos, Tessa Lau, Wendy Kellogg, Catalina Danis, Nicholas Kushmerick. Taxie: Automatically identifying tasks in email. Unpublished Manuscript, 2004.

 2003 (1 Publications) Kevin Livingston, Mark Dredze, Kristian Hammond, Larry Birnbaum. Beyond Broadcast. Proceedings of the 2003 International Conference on Intelligent User Interfaces, 2003.

 Masters Thesis For my masters thesis in Jewish Studies at Yeshiva University, I completed a thesis titled: The Values of Traditional Judaism in Chicago. Please email me if you'd like a copy of this thesis.

## Students

Current Students

 Nicholas Andrews (Co-advised with Jason Eisner) Matt Gormley [www] (Co-advised with Jason Eisner) Michael Paul [www] (Co-advised with Jason Eisner) Travis Wolfe [www] Violet (Nanyun) Peng

Former Students
 Ariya Rastrow [www] (Co-advised with Sanjeev Khudanpur). ECE PhD, 2012. First Job: Amazon. Carolina Parada [www] (Co-advised with Hynek Hermansky). ECE PhD, 2011. First Job: Google Research.

 Project Student Year Information Extraction from Biomedical Text Leah Hanson 2011

 Project Student Email keyword summarization Danny Puller UPenn Summer Provost Fellowship Sentiment classification Ian Cohen Email Attachment Prediction Josh Magarick Prototype Driven Learning and Graphical Models Neal Parikh Machine Learning in Prediction Markets Ari Gilder Kevin Lerman Winner Best CS Senior Design Project, Honorary Mention Best Engineering Design Project User Adaptation in Email Reply Prediction Tova Brooks Josh Carroll Formal and Informal Meeting Extraction from Email Lauren Paone

## Teaching

Fall 2012: CS 600.475 Current Topics in Machine Learning [Class site]
Spring 2012: CS 600.775 Current Topics in Machine Learning [Class site]
Fall 2011: CS 600.475 Machine Learning [Class site]
Spring 2011: CS 600.775 Current Topics in Machine Learning [Class site]
Fall 2010: CS 600.475 Machine Learning [Class site]
Fall 2009: CS 600.475 Machine Learning [Class site]

## Data/Code

I get a lot of emails asking me for data or code from one of my papers. If you are wondering, the answer is yes! I try to provide both data and code so that others can reproduce or compare against my results. Sadly, I don't post data or code due to the lack of time, but I usually make them available if you email me.

Datasets
TAC 2009 Entity Linking (Email for data)
A collection of manually linked training examples to supplement those provided in the TAC 2009 KBP task. These are described in my Coling 2010 paper on entity linking.

A collection of ham and spam images taken from real user email.

Product reviews from several different product types taken from Amazon.com.

Attachment Prediction Email (Email for data)
Enron emails annotated with attachment information and cleaned of numerous artificats inserted by email programs.

Code
This is a collection of software developed by me and others in Fernando Pereria's research group at UPenn. It is designed for a range of machine learning tasks, such as dependency parsing, structured learning, gene prediction and gene mention finding.

Confidence Weighted Learning Library (Email for code)
We have collected most of the core algorithms in the confidence weighted learning framework for release as a software library. Please email me for the code.

Carmen is a library for geolocating tweets. Given a tweet, Carmen will return Location objects that represent a physical location. Carmen uses both coordinates and other information in a tweet to make geolocation decisions. It's not perfect, but this greatly increases the number of geolocated tweets over what Twitter provides.

## Colleagues

I have worked with a lot of amazing people on a wide variety of projects. Here are a few of them:

 Kedar Bellare Axel Bernal Larry Birnbaum John Blitzer Koby Crammer Krzysztof Czuba Kris Hammond Ryan Gabbard Kuzman Ganchev João Graça David Johnson Rie Johnson (Ando) Alex Kulesza Nicholas Kushmerick Tessa Lau Kevin Lerman Qian Liu Ryan McDonald David Mimno Peter Norvig Fernando Pereira Jeff Reynar Doug Riecken Sam Roweis Bill Schilit Partha Pratim Talukdar Hanna M. Wallach Joel Wallenberg Casey Whitelaw Tong Zhang

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# Value of a Statistical Life under Large Mortality Risk Change: Theory and an Application to COVID-19

Diego S. Cardoso and Ricardo Dahis. (R&R)

Abstract In the estimation of the benefits of mortality reduction, a simple approach is to multiply the value of a statistical life (VSL) by the expected reduction in fatalities, thus holding the VSL constant. This procedure approximates benefits for small changes in mortality, but inaccurately characterizes benefits for large risk changes because it does not account for variations in the VSL. Building on the theoretical framework of the VSL, we outline a practical approach to calculate the benefits of large mortality reductions. This approach is readily applicable, yielding closed-form expressions that only require statistics broadly available for VSL-based calculations. Using recent empirical estimates of the VSL, we apply this approach to estimate the benefits of social distancing to combat COVID-19 in the United States and Brazil, two of the countries most affected by the pandemic. Our findings show that social distancing generates a benefit of \$4–4.4 trillion in the United States, and \$0.6 trillion in Brazil. We extend this analysis to other 72 countries using VSL projections and find that benefits correspond to 17% of the gross national income on average. Our results indicate that the constant VSL approach overestimates the benefits of social distancing by 74% on average.


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ROUGH DRAFT authorea.com/96993

An algorithm for calculating reservoir pressure from the arterial pressure waveform

Introduction

The reservoir-wave hypothesis was introduced in order to resolve a number of difficulties in the interpretation of arterial mechanics. The capacitive nature of the compliant arteries was recognised by Borelli (Borelli 1680), popularised by Hales (Hales 1733) and made quantitative by Frank (Frank 1899). The Windkessel model introduced by Frank is an excellent representation of arterial mechanics during diastole when the arterial flow is isolated from the left ventricle by the closed aortic valve. However, despite decades of work, the Windkessel model could not describe the observed pressure and flow in the arteries during systole. As a result, it fell into disfavour.

The wave nature of arterial mechanics was obvious to the earliest observers and a great deal of attention was given to the arterial pulse. In the second half of the twentieth century, the wave analysis of arterial mechanics became the dominant interpretation of arterial physiology. The measured pressure and flow were Fourier analysed and state of the cardiovascular system was characterised by its impedance, derived by dividing the pressure coefficient by the flow coefficient for each frequency. This method proved to be a very powerful descriptor of the state of the cardiovascular system (Nichols).

The impedance method provided a way of separating the measured pressure and flow waveforms into forward and backward travelling waves. This is very convenient in the arterial system with the ventricle at the proximal end of the arteries and the highly resistive microcirculation at the distal end. This separation into forward and backward waves has also been very successful in describing many of the puzzling features of the mechanics of the arterial system. Features that can be explained include the difference between the flow and pressure waveforms, the increase in pulse pressure in more distal arteries and the change in pressure waveform with age [Refs to be included??]. There are, however, some features of the separated forward and backward waves that are difficult to explain. Most prominent of these difficulties is the large wave that occurs during diastole in which the two pressure waves sum to give the diastolic decrease in pressure that is characteristic of all pressure waveforms and the two flow waves subtract to give the very small flow waveforms that is characteristic of all flow waveforms during diastole.

The reservoir-wave hypothesis resolves this problem by assuming that the measured arterial pressure $$P$$ is the sum of a reservoir pressure $$\bar{P}$$ reflecting the capacitive nature of the arteries and an excess pressure $$p$$ describing their wave nature. Under this hypothesis, the exponential decay of pressure during diastole is described by the reservoir pressure and the excess pressure during diastole is very small, similar to the flow. The reservoir pressure is not exactly the Windkessel pressure described by Frank’s theory, which assumes that the wave speed in the arteries is infinite, because it is seen as the result of waves and can vary from place to place in the arterial system, unlike the Windkessel pressure. There have been several attempts to define the reservoir pressure theoretically but none are entirely satisfactory. It is best seen as an ad hoc hypothesis to be judged by its usefulness in explaining physiological and pathological arterial behaviour. Because of this, it is important to have a well-defined algorithm for calculating the reservoir pressure from the measured arterial pressure.

In the original presentation of the reservoir-wave hypothesis (Wang 2003), the reservoir pressure was calculated from the flow waveform that was simultaneously measured. It was recognised that the difficulty in simultaneously measuring pressure and flow clinically is a serious limitation of the method. Recently, we published a method for estimating the reservoir pressure using only the measured pressure waveform (J. 2007). This method was based on the observation in canine experiments that the excess (or wave) pressure waveform in the ascending aorta, derived by subtracting the flow-derived reservoir pressure from the measured pressure, was very nearly proportional to the measured flow; the constant of proportionality being the characteristic impedance of the aorta. The results obtained by this method, applied in a number of arteries in both canines and humans, were encouraging. We have found, however, that the application of the method described in that paper resulted in the failure of the algorithm in an unacceptable fraction of cases. This problem is also reported in another study comparing the reservoir pressure to the windkessel pressure obtained from a three-element windkessel model (Vermeersch 2009). In this study, they report that the reservoir pressure algorithm failed in 307 of 2019 subjects.

This paper describes a new Matlab algorithm for calculating reservoir pressure from a measured pressure waveform. It is based on the same principles as the previous algorithm but incorporates a number of differences that make it much more robust. The major changes include a method for fitting the pressure during diastole based on exponential moments and a global parameter fitting iteration that minimises the sensitivity of the method to the accurate determination of the end of systole and start of diastole.

Theory

theoretical background

As in our previous paper (J. 2007), the calculation of the reservoir pressure is based upon the mass conservation arguments used by Frank in the derivation of the windkessel model

$$\frac{dV}{dt}=Q_{in}-Q_{out},\nonumber \\$$

where $$V$$ is the volume of the arterial system, $$Q_{in}$$ is the volume flow rate into the arteries from the ventricle and $$Q_{out}$$ is the volume flow rate out of the arteries through the microcirculation. We assume that the compliance of the arteries, $$\mathcal{C}=\frac{dV}{d\bar{P}}$$, is constant and that flow through the microcirculation can be describe by the linear relationship

$$Q_{out}=\frac{\bar{P}-P_{\infty}}{\mathcal{R}},\nonumber \\$$

where $$P_{\infty}$$ is the pressure at which flow through the microcirculation ceases and $$\mathcal{R}$$ is the net resistance of the microcirculation. We note that $$P_{\infty}$$ is not necessarily the venous pressure but could be related to the interstitial pressure through a waterfall effect. In the algorithm, $$P_{\infty}$$, $$\mathcal{R}$$ and $$\mathcal{C}$$ are treated as model parameters. With these assumptions, the mass conservation equation can be written

$$\label{eqwkflow}\frac{d(\bar{P}-P_{\infty})}{dt}+b(\bar{P}-P_{\infty})=\kappa Q_{in}\nonumber \\$$

where $$b=1/\mathcal{RC}$$, $$\kappa=1/\mathcal{C}$$ and we have assumed that $$P_{\infty}$$ does not vary with time.

This equation can be solved by quadrature for any $$Q_{in}(t)$$ using the integrating factor $$e^{\frac{t}{\mathcal{RC}}}$$, which leads to

$$\bar{P}-P_{\infty}=\kappa e^{-bt}\int_{0}^{t}Q_{in}(t^{\prime})e^{bt^{\prime}}dt^{\prime}+e^{-bt}(\bar{P}_{d}-P_{\infty}),\\$$

where $$\bar{P}_{d}=\bar{P}(0)$$ is the reservoir pressure at $$t=0$$, taken as the end diastolic time. This is the classical result which has been used to calculate the reservoir pressure when the volume flow rate into the arteries $$Q_{in}$$ is measured.

We note that the solution is particularly simple during diastole when $$Q_{in}=0$$. If we define $$P_{N}=\bar{P}(T_{n})$$ as the pressure at $$t=T_{n}$$, the time of the dicrotic notch which denotes the start of diastole, then the solution during diastole is

$$\label{eqwkflowdiastole}\bar{P}-P_{\infty}=e^{-b(t-T_{n})}(P_{N}-P_{\infty}),\qquad T_{n}\leq t\leq T,\\$$

where $$T$$ is the cardiac period.


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# Can a manifold have a curvature-free connection that is not torsion-free?

Suppose I have a smooth manifold with a tangent bundle, and I have a connection. If this connection is curvature-free, is it guaranteed to be torsion-free? (I am not assuming a metric, just a finite-dimensional smooth manifold.)

I know that in general curvature-free connections do not exist, and that in general torsion-free connections do. But is the existence of a curvature-free connection sufficient to prove the existence of a torsion-free connection?

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Torsion is defined only for connections on the tangent bundle. –  Tom Goodwillie Nov 21 '13 at 1:51
Typo. I just fixed it. –  pavpanchekha Nov 21 '13 at 1:52

Many manifolds have curvature-free (i.e., flat) connections on their tangent bundles. For example, any orientable $3$-manifold $M$ is parallelizable, i.e., its tangent bundle is trivial, so it carries a flat connection (in fact, many flat connections). However, nearly all of these connections will have torsion. In fact, they all will unless $M$ is very special; essentially, $M$ has to be what is known as a flat affine manifold.

I don't understand your last question. For example, the $3$-sphere has a flat connection (just regard it as a Lie group and take the connection that makes the left-invariant vector fields parallel). However, the $3$-sphere certainly does not admit a torsion-free flat connection, since it is compact and simply-connected.

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Special examples of Robert Bryants answer are Lie groups. On any Lie group, the left trivialization of the tangent bundle corresponds to a flat connection whose torsion is essentially the Lie bracket, whereas the right trivialization corresponds to another connection without curvature whose torsion is essentially the negative of the Lie bracket.

On the other hand any inner product on the Lie algebra gives rise to a right invariant metric (biinvariant if the inner product is invariant under the adjoint representation). Its Levi-Civita connections is torsion free and it has a very interesting curvature (See a paper by Milnor for the finite dimensional case or the famous paper by Arnold on volume preserving diffeomorphisms).

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I believe that this is the Milnor's paper: J. Milnor, Curvatures of Left Invariant Metrics on Lie Groups, Advances in Math (sciencedirect.com/science/article/pii/S0001870876800023) –  Vít Tuček Nov 21 '13 at 21:05

A bundle $E\rightarrow M$ will have a curvature free connection if it is trivial on some covering space $\pi:\tilde{M}\rightarrow M$ (one can take the universal cover) of $M$.

Vanishing curvature form $\Omega=d\omega+[\omega,\omega]$, $\omega$ is the connection form, means the horizontal spaces on the principal bundle $L_{GL(n)} \rightarrow M$ are tangent to a foliation.

The torsion is only defined when $E=TM$. A flat connection will usually not be torsion free. Locally, a flat connection is given by a trivializing frame field $(X_1,\ldots,X_n)$, a parallel frame. In this case $X_i$ are vector fields. But the connection will only be torsion free if $[X_i,X_j]=0,\ \forall i,j$.

The answer to your first question is a strong negative. Since any local frame field locally defines a flat connection, which has torsion if they don't commute.

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### Home > A2C > Chapter 6 > Lesson 6.2.5 > Problem6-119

6-119.

Solve $5^{x} = 15$ for $x$. Be accurate to two decimal places|. .

Use the eTool below to solve for x.
Click the link to the right for full version. 6-119 HW eTool


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# References for the bicategory of ring-bimodule pairs

One of the standard examples of a bicategory is the bicategory of rings (with bimodules as 1-morphisms), which is sometimes denoted $\operatorname{Bim}$ and in other sources $\operatorname{Ring}$ (or $\operatorname{Alg}$ if you are working over a commutative ground ring $k$). It is defined as follows:

• objects are rings
• the category of 1-morphisms from $R$ to $R'$ is the category of $R'$-$R$-bimodules (I'm working with left modules, but that doesn't really matter.)
• composition is given by the tensor product of bimodules
• 2-morphisms are bimodule homomorphisms
• 2-composition is the usual composition of bimodule homomorphisms.

In my research, the following bicategory appeared "naturally" (I would like to call it $\operatorname{RingMod}$, resp. $\operatorname{AlgMod}$):

• objects are pairs $(R,N)$ where $R$ is a ring and $N$ is an $R$-module
• a $1$-morphism $(R,N)\to (R',N')$ is a pair $(M,g)$ where $M$ is an $R'$-$R$-bimodule and $g\colon M\otimes_R N\to N'$ is an $R'$-linear map
• 1-composition is given by $(M',g')\circ (M,g)=(M'\otimes_{R'} M,g'(M'\otimes_{R'} g))$
• 2-morphisms $\psi\colon (M,g)\to (\tilde{M},\tilde{g})$ are bimodule homomorphisms such that $\tilde{g} (\psi\otimes \operatorname{id}_N)=g$

I have two questions:

1. [A bit vague] Does this seem like the correct notion of a 2-morphism in this bicategory or does it seem too strong?
2. Did this bicategory already appear somewhere in the literature (possibly with a different notion of 2-morphism)?
• Is that the slice 2-category over $\mathbf{Z}$? – Pavel Safronov Mar 25 '15 at 17:01
• @PavelSafronov That's a nice observation. If I understand it correctly from the brief look I took at the definition it should be a lax slice $2$-category over $\mathbb{Z}$ (resp. $k$). – Julian Kuelshammer Mar 25 '15 at 17:09
• Section 5.3 in arxiv.org/pdf/1106.5665.pdf looks very similar to what you have, but I am not sure if you have a special case they were using. – Aaron Chan Mar 31 '15 at 11:59


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Bolt holes, burr and fatigue life effects

Help Support HomeBuiltAirplanes.com:

wktaylor

Well-Known Member
OH YEAH... 'WM'... even though I know You are kidding...

RE: "Only half the rivets need to hold, so that cuts down workload."

Question… Which half-of-the-rivets in a row should 'hold'... front half, back half, middle half, every-other, groups of '3s'... etc?

Recently my organization encountered a ridiculous problem... we discovered that one/more replacement critical upper skin panels LH & RH wings on our many Depot maintenance jets... since 2012... were installed using highly non-standard/un-documented methods to speed-up production. A high majority of the rivets were irregularly bucked... every discrepancy imaginable.

Us engineers were asked: what is safety margin if 1/2 of the rivets were installed unsatisfactorily [other than fuel leaking]. Since riveting has become so standardized over the decades... and the 'acceptable limits' were tested and validated long-ago... and since rivets are accessible for detailed visual inspection... there was never any issue [reports, standards, etc] regarding wide-scale substandard workmanship... other-than rules such as "1-in10 questionable rivet installs... randomly scattered and no [3] clustered together".

The only answer we all had was this is a critical airworthiness issue.... going back 8-years worth of Depot maintenance. Mandatory removal and replacement of millions of discrepant rivets, under urgent circumstances, will cost millions of s, is warranted. EVEN the 'good' rivets have to be replaced since they have been disproportionately stressed... absorbing load/strain that 'bad rivets' should have been carrying.

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TuweetyBird

Member
HBA Supporter
Google Navair NA 01-1A-1....just happened to remember this guy as I was reading through here, plenty of PDFs available...be safe.

wktaylor

Well-Known Member
USAF => 1-1A-1 Engineering Handbook Series For Aircraft Repair - General Manual For Structural Repair [co-numbered 01-1A-1] Technical Orders

USAF => 1-1A-8 Engineering Manual Series - Aircraft and Missile Repair - Structural Hardware [co-numbered 01-1A-8]

wktaylor

Well-Known Member
BTW... TuweetyBird for grins...

In the 1980s [~4-years], I was USAF lead structural/mechanical engineer [SA-ALC, Kelly AFB] for the T-37 Tweetybird and the A/OA-37B Dragonfly. Great experiences... lots of mishap training, though...

SamP

Member
Thanks for everyone's input. My brain is slowly "grokking." Just trying to build the right model in my brain so all the factoids I pick up fit together.

Interestingly, I learned as I read through materials that while differences in clearance may affect how much load a particular bolt takes, oversized holes don't affect ultimate failure or maximum load, but does affect fatigue failure. I also learned that maximum load drops dramatically if your don't drill perpendicular to the surface.

From my understanding, bolt joints are designed to hold pieces together, and that friction between the two pieces is the predominant factor, not shear strength of a bolt. That friction is created by the bolt preload (how hard you are cranking down on the nut). I also know that the preload on bolts are pretty low for aircraft applications, implying that it is the bolt getting loaded in shear that is doing much of the work. Is there a contradiction there?

Edit: I just read that some shear joints are designed for the bolt itself to take the shear, and not the friction between the plates. It would seem that the aircraft joints are designed according to this So maybe no contradiction.

There is also what entails joint failure. Is it the bolt shearing (then oversizing shouldn't affect the failure since it's the same bolt), or maybe slippage / deformation. There is rabbit hole I'm definitely going down...

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Matt G.

Well-Known Member
No contradiction. In reality, the friction does do some of the work, but in analysis of aircraft structure it is always neglected (perhaps I should say almost always...someone here probably has an exception I have yet to encounter in my career), and assumed 100% of the load is transmitted between the two halves of the joint through fastener shear.

The failure mode of the bolted joint will depend on a lot of things, such as the materials being joined together, their thicknesses, the type, material, and diameter of the fastener, the thickness of the stackup of parts in the joint, and fastener spacing and distance from a free edge, among other things. Oversizing will increase the bearing strength of the sheet at that location, as well as the fastener shear, but having one larger fastener can cause issues in a fatigue critical area. If it is significantly oversized and the fasteners are close together, net area across the joint may become an issue, depending on the structure and loading.

The nice thing is that if you stick with recommended design practices and choose fasteners and materials that are commonly used for aircraft, there are joint allowables that have been developed by test that have many of these things built in.

robertbrown

Member
Doing mod & repair work on large transport aircraft, a lot of poor workmanship would be uncovered as structure was disassembled. Someone remarked that instead of analyzing for a "rogue flaw" that was a .010" long crack in a fastener hole, we should assume that the rogue flaw was a large chisel mark in the hole. I would say that workmanship on the wing was better; critical holes there were classified as "Fracture & Fatigue Critical" and had more stringent inspections.

gtae07

Well-Known Member
This mechanical engineer sees a bunch of individual issues that are taught to mechanical engineers:
I wish those things were taught to aerospace engineers, too. Unfortunately it seems the AE schools (or at least mine) see fit to leave that kind of "practical stuff" for employers/industry to teach. Gets in the way of theory and research, you see...

Reading accident reports for cases caused by minor manufacturing defects is sobering...

robertbrown

Member
I wish those things were taught to aerospace engineers, too. Unfortunately it seems the AE schools (or at least mine) see fit to leave that kind of "practical stuff" for employers/industry to teach. Gets in the way of theory and research, you see...

Reading accident reports for cases caused by minor manufacturing defects is sobering...
I had a boss who had been an AE student when he got a job as a draftsman. He couldn't manage working and going to school at the same time so he dropped out. At that time, they didn't have any problem with promoting draftsmen to design engineers and he finished up his career as an engineering supervisor. He said that it had worked out well because most of the AE's wound up in preliminary design or aerodynamics while more ME's got to work with the aircraft structure and he preferred being close to the aircraft. They don't tell you all that in school. We had several engineering coop students who changed majors after working with us but I'd rather blame that on pushing STEM on everyone regardless of individual personalities than on us being like Dilbert's office.


text:
# New Golden Ratio Construct: which one of my constructs is superior/simplest--squares & circles or just circles?

I have found yet another golden ratio construction. Geogebra gives it the value of 1.61803398874990 to the ratio between the yellow and blue lines in the figure below, which is the golden ratio PHI. :) Have you seen these constructions anywhere else? And a geometric/trigonometric proof would be great for any/all of these! :) Which is the best/your favorite?

Which way do you think is the simplest/best way to construct the golden ratio in this case?

The first construction: 1. Draw a square. 2. Draw a circle with diameter the side of a square. 3. Draw a circle of the same diameter on a far corner of the square. 4. Draw a line through the centers of the circles, and the golden cut appears as drawn.

Second construction: 1. Draw a circle. 2. Draw a circle of equal diameter tangent to it on the right. 3. Draw a third circle on a line perpendicular to the line connecting the centers of the first two circles so that the third circle's center intersects with the circumference of the first circle. 4. Draw a line through the centers of the first and third circles, and the golden cut appears as drawn.

Third construction: 1. Draw a circle. 2. Draw a second circle with the same diameter centered on the circumference of the first circle. 3. Draw a third circle directly below the second circle with the same diameter centered on the circumference of the second circle. 4. Draw a fourth circle directly to the right of the third circle with the same diameter centered on the circumference of the third circle. 5. Draw a line through the centers of the first and fourth circles, and the golden cut appears as drawn.

I like this construction (these constructions) because a) the golden cut is defined by the actual shapes drawn, instead of a line added in, and b) the final step is just drawing a line through the centers of the first and last circle drawn. Pretty simple!

Have you seen these constructions anywhere else? And a geometric/trigonometric proof would be great for any/all of these! :) Which is the best/your favorite?

And perhaps you may even see a simpler way to construct it!

By the "secant-tangent" aspect of the Power of a Point theorem, we have $$|\overline{AB}||\overline{AC}| = |\overline{AD}|^2 \quad\to\quad q\cdot(p+q) = p^2 \quad\to\quad \frac{p}{q} = \frac{p+q}{p} = \frac{|\overline{BC}|}{|\overline{AB}|}$$

The relation between $p$ and $q$ says exactly that $\frac{p}{q}$ is the golden ratio, $1.618\dots$.

The first construction can be shown to given the golden ratio by considering ($a$ is the length of the sides of the square):
$$\phi=\frac{a}{\sqrt{(a/2)^2+a^2}-a/2}=\frac{1+\sqrt{5}}{2}$$
• Also, in the first two pictures, $\frac{\text{blue}+\text{yellow}}{\text{side of square}}=\phi$ May 3 '16 at 6:38