models-and-modes   136

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[1802.07029] On a fully fuzzy framework for minimax mixed integer linear programming
In this work, we present a modeling framework for minimax mixed 0-1 fuzzy linear problems. It is based on extending the usual rewriting of crisp minimax problems via auxiliary variables to model the maximum of a finite set of fuzzy linear functions. We establish that the considered problem can be equivalently formulated as a multiple objective mixed integer programming problem. The framework is applied to a fully fuzzy version of the capacitated center facility location problem.
representation  fuzzy  operations-research  integer-programming  models-and-modes  to-write-about  could-be-clearer 
yesterday by Vaguery
Explainable AI won’t deliver. Here’s why. – Hacker Noon
Imagine choosing between two spaceships. Spaceship 1 comes with exact equations explaining how it works, but has never been flown. How Spaceship 2 flies is a mystery, but it has undergone extensive testing, with years of successful flights like the one you’re going on.
Which spaceship would you choose?
This is a philosophical question, so I can’t answer it for you. I know I have a personal preference — maybe that’s the statistician in me — but I would choose careful testing as a better basis for trust.
artificial-intelligence  explanation  philosophy-of-engineering  maintainability  models-and-modes  that-taco-bell-girl-says-what? 
yesterday by Vaguery
Quantum Epistemology for Business - Scientific American Blog Network
We are laboring under the illusion of “classical information” and “classical measurement” when we deal with human and organizational phenomena. Insights from quantum epistemology raise indelible doubts about the given-ness of data. But businesspeople need more than reasonable doubt: they need insights and action prompts. How can we leverage “quantum effects” in human organizations? The “quantum epistemology of social phenomena” is in its infancy, but already can provide a battery of new questions for those who want to understand and shape the process of measurement.
planning  define-your-terms  rather-interesting  models-and-modes  to-write-about  management 
5 weeks ago by Vaguery
THE EXTENDED EVOLUTIONARY SYNTHESIS | Proceedings of the Royal Society of London B: Biological Sciences
Scientific activities take place within the structured sets of ideas and assumptions that define a field and its practices. The conceptual framework of evolutionary biology emerged with the Modern Synthesis in the early twentieth century and has since expanded into a highly successful research program to explore the processes of diversification and adaptation. Nonetheless, the ability of that framework satisfactorily to accommodate the rapid advances in developmental biology, genomics and ecology has been questioned. We review some of these arguments, focusing on literatures (evo-devo, developmental plasticity, inclusive inheritance and niche construction) whose implications for evolution can be interpreted in two ways—one that preserves the internal structure of contemporary evolutionary theory and one that points towards an alternative conceptual framework. The latter, which we label the ‘extended evolutionary synthesis' (EES), retains the fundaments of evolutionary theory, but differs in its emphasis on the role of constructive processes in development and evolution, and reciprocal portrayals of causation. In the EES, developmental processes, operating through developmental bias, inclusive inheritance and niche construction, share responsibility for the direction and rate of evolution, the origin of character variation and organism–environment complementarity. We spell out the structure, core assumptions and novel predictions of the EES, and show how it can be deployed to stimulate and advance research in those fields that study or use evolutionary biology.
evolutionary-biology  academic-culture  models-and-modes  theoretical-biology  define-your-terms  rather-interesting  to-write-about 
10 weeks ago by Vaguery
[1809.10756] An Introduction to Probabilistic Programming
This document is designed to be a first-year graduate-level introduction to probabilistic programming. It not only provides a thorough background for anyone wishing to use a probabilistic programming system, but also introduces the techniques needed to design and build these systems. It is aimed at people who have an undergraduate-level understanding of either or, ideally, both probabilistic machine learning and programming languages.
We start with a discussion of model-based reasoning and explain why conditioning as a foundational computation is central to the fields of probabilistic machine learning and artificial intelligence. We then introduce a simple first-order probabilistic programming language (PPL) whose programs define static-computation-graph, finite-variable-cardinality models. In the context of this restricted PPL we introduce fundamental inference algorithms and describe how they can be implemented in the context of models denoted by probabilistic programs.
In the second part of this document, we introduce a higher-order probabilistic programming language, with a functionality analogous to that of established programming languages. This affords the opportunity to define models with dynamic computation graphs, at the cost of requiring inference methods that generate samples by repeatedly executing the program. Foundational inference algorithms for this kind of probabilistic programming language are explained in the context of an interface between program executions and an inference controller.
This document closes with a chapter on advanced topics which we believe to be, at the time of writing, interesting directions for probabilistic programming research; directions that point towards a tight integration with deep neural network research and the development of systems for next-generation artificial intelligence applications.
via:jar  programming-language  representation  probability-theory  models-and-modes  semantics  syntax  pragmatics  to-write-about 
10 weeks ago by Vaguery
A non-spatial account of place and grid cells based on clustering models of concept learning | bioRxiv
One view is that conceptual knowledge is organized as a "cognitive map" in the brain, using the circuitry in the medial temporal lobe (MTL) that supports spatial navigation. In contrast, we find that a domain-general learning algorithm explains key findings in both spatial and conceptual domains. When the clustering model is applied to spatial navigation tasks, so called place and grid cells emerge because of the relatively uniform sampling of possible inputs in these tasks. The same mechanism applied to conceptual tasks, where the overall space can be higher-dimensional and sampling sparser, leads to representations more aligned with human conceptual knowledge. Although the types of memory supported by the MTL are superficially dissimilar, the information processing steps appear shared.
models-and-modes  emergence  data-analysis  rather-interesting  to-write-about  consider:the-mangle 
11 weeks ago by Vaguery
The Only Woman to Win the Nobel Prize in Economics Also Debunked the Orthodoxy - Evonomics
I mention Lloyd’s essay to illustrate how ridiculous yet persistent the misconceptions about the “tragedy” dynamic truly are. Commons scholar Lewis Hyde dryly notes, “Just as Hardin proposes a herdsman whose reason is unable to encompass the common good, so Lloyd supposes persons who have no way to speak with each other or make joint decisions. Both writers inject laissez-faire individualism into an old agrarian village and then gravely announce that the commons is dead. From the point of view of such a village, Lloyd’s assumptions are as crazy as asking us to ‘suppose a man to have a purse to which his left and right hand may freely resort, each unaware of the other’.”

This absurdity, unfortunately, is the basis for a large literature of “prisoner’s dilemma” experiments that purport to show how “rational individuals” behave when confronted with “social dilemmas,” such as how to allocate a limited resource. Should the “prisoner” cooperate with other potential claimants and share the limited rewards? Or should he or she defect by grabbing as much for himself as possible?
economics  ideology  public-policy  models-and-modes  commons  to-write-about  theory-and-practice-sitting-in-a-tree  libertarianism  assumptions 
may 2018 by Vaguery
Props in Network Theory | Azimuth
We start with circuits made solely of ideal perfectly conductive wires. Then we consider circuits with passive linear components like resistors, capacitors and inductors. Finally we turn on the power and consider circuits that also have voltage and current sources.

And here’s the cool part: each kind of circuit corresponds to a prop that pure mathematicians would eventually invent on their own! So, what’s good for engineers is often mathematically natural too.

commentary: while abstract, it might be worth trying to understand this stuff
network-theory  abstraction  rather-interesting  models-and-modes  circles-and-arrows  bond-diagrams  to-write-about  to-understand  functional-programming  category-theory  via:Vaguery 
april 2018 by WMTrenfield
Props in Network Theory | Azimuth
We start with circuits made solely of ideal perfectly conductive wires. Then we consider circuits with passive linear components like resistors, capacitors and inductors. Finally we turn on the power and consider circuits that also have voltage and current sources.

And here’s the cool part: each kind of circuit corresponds to a prop that pure mathematicians would eventually invent on their own! So, what’s good for engineers is often mathematically natural too.
network-theory  abstraction  rather-interesting  models-and-modes  circles-and-arrows  bond-diagrams  to-write-about  to-understand  functional-programming  category-theory 
april 2018 by Vaguery
The meaning of model equivalence: Network models, latent variables, and the theoretical space in between | Psych Networks
Recently, an important set of equivalent representations of the Ising model was published by Joost Kruis and Gunter Maris in Scientific Reports. The paper constructs elegant representations of the Ising model probability distribution in terms of a network model (which consists of direct relations between observables), a latent variable model (which consists of relations between a latent variable and observables, in which the latent variable acts as a common cause), and a common effect model (which also consists of relations between a latent variable and observables, but here the latent variable acts as a common effect). The latter equivalence is a novel contribution to the literature and a quite surprising finding, because it means that a formative model can be statistically equivalent to a reflective model, which one may not immediately expect (do note that this equivalence need not maintain dimensionality, so a model with a single common effect may translate in a higher-dimensional latent variable model).

However, the equivalence between the ordinary (reflective) latent variable models and network models has been with us for a long time, and I therefore was rather surprised at some people’s reaction to the paper and the blog post that accompanies it. Namely, it appears that some think that (a) the fact that network structures can mimic reflective latent variables and vice versa is a recent discovery, that (b) somehow spells trouble for the network approach itself (because, well, what’s the difference?). The first of these claims is sufficiently wrong to go through the trouble of refuting it, if only to set straight the historical record; the second is sufficiently interesting to investigate it a little more deeply. Hence the following notes.
dynamical-systems  models  models-and-modes  representation  philosophy-of-science  (in-practice)  to-write-about  via:several 
april 2018 by Vaguery
Church vs Curry Types - LispCast
My ambitious hope is that this perspective will quiet a lot of the fighting as people recognize that they are just perpetuating a rift in the field of mathematics that happened a long time ago. The perspectives are irreconcilable now, but that could change. A paper called Church and Curry: Combining Intrinsic and Extrinsic Typing builds a language with both kinds of types. And Gradual Typing and Blame Calculus are investigating the intersection of static and dynamic typing. Let’s stop fighting, make some cool tools and use them well.
type-theory  computer-science  models-and-modes  dichotomy-or-not?  to-write-about 
march 2018 by Vaguery
Opinion | Corporate America Is Suppressing Wages for Many Workers - The New York Times
For a long time, economists believed that labor-market monopsony rarely existed, at least outside old-fashioned company towns where a single factory employs most of the residents. But in recent decades, several compelling studies have revealed that monopsony is omnipresent. Professionals like doctors and nurses, workers in factories and meat processing plants, and sandwich makers and other low-skill workers earn far less — thousands of dollars less — than they would if employers did not dominate labor markets.

The studies show that common features of the labor market give enormous bargaining advantages to employers. Because most people sink roots in their communities, they are reluctant to quit their job and move to a job that is far away. Because workplaces differ in terms of their location and conditions, people have trouble comparing them, which means that one cannot easily “comparison shop” for jobs. And thanks to a wave of consolidation, industries are increasingly dominated by a small number of huge companies, which means that workers have fewer choices among employers in their area.

When employers exercise monopsonistic power, wages are suppressed, jobs are left unfilled, and economic growth suffers. Unions used to offset employer monopsony power, but unions now represent only 7 percent of private sector workers, down from a peak of 35 percent in the 1950s. Combating the practices that employers use to monopsonize the labor market can lead to higher wages, more jobs and faster economic growth.
worklife  economics  models-and-modes  public-policy  power-relations  to-write-about  capitalism 
march 2018 by Vaguery
[1802.02627] Going Deeper in Spiking Neural Networks: VGG and Residual Architectures
Over the past few years, Spiking Neural Networks (SNNs) have become popular as a possible pathway to enable low-power event-driven neuromorphic hardware. However, their application in machine learning have largely been limited to very shallow neural network architectures for simple problems. In this paper, we propose a novel algorithmic technique for generating an SNN with a deep architecture, and demonstrate its effectiveness on complex visual recognition problems such as CIFAR-10 and ImageNet. Our technique applies to both VGG and Residual network architectures, with significantly better accuracy than the state-of-the-art. Finally, we present analysis of the sparse event-driven computations to demonstrate reduced hardware overhead when operating in the spiking domain.
via:?  to-write-about  to-read  neural-networks  representation  models-and-modes  machine-learning  simulation 
february 2018 by Vaguery
[1703.10651] Reliable Decision Support using Counterfactual Models
Making a good decision involves considering the likely outcomes under each possible action. For example, would drug A or drug B lead to a better outcome for this patient? Ideally, we answer these questions using an experiment, but this is not always possible (e.g., it may be unethical). As an alternative, we can use non-experimental data to learn models that make counterfactual predictions of what we would observe had we run an experiment. To learn such models for decision-making problems, we propose the use of counterfactual objectives in lieu of classical supervised learning objectives. We implement this idea in a challenging and frequently occurring context, and propose the counterfactual GP (CGP), a counterfactual model of continuous-time trajectories (time series) under sequences of actions taken in continuous-time. We develop our model within the potential outcomes framework of Neyman and Rubin. The counterfactual GP is trained using a joint maximum likelihood objective that adjusts for dependencies between observed actions and outcomes in the training data. We report two sets of experimental results. First, we show that the CGP's predictions are reliable; they are stable to changes in certain characteristics of the training data that are not relevant to the decision-making problem. Predictive models trained using classical supervised learning objectives, however, are not stable to such perturbations. In the second experiment, we use data from a real intensive care unit (ICU) and qualitatively demonstrate how the CGP's ability to answer "What if?" questions offers medical decision-makers a powerful new tool for planning treatment.
machine-learning  models-and-modes  rather-interesting  to-write-about  consider:symbolic-regression 
january 2018 by Vaguery
[1710.03453] The Sparse Multivariate Method of Simulated Quantiles
In this paper the method of simulated quantiles (MSQ) of Dominicy and Veredas (2013) and Dominick et al. (2013) is extended to a general multivariate framework (MMSQ) and to provide a sparse estimator of the scale matrix (sparse-MMSQ). The MSQ, like alternative likelihood-free procedures, is based on the minimisation of the distance between appropriate statistics evaluated on the true and synthetic data simulated from the postulated model. Those statistics are functions of the quantiles providing an effective way to deal with distributions that do not admit moments of any order like the α-Stable or the Tukey lambda distribution. The lack of a natural ordering represents the major challenge for the extension of the method to the multivariate framework. Here, we rely on the notion of projectional quantile recently introduced by Hallin etal. (2010) and Kong Mizera (2012). We establish consistency and asymptotic normality of the proposed estimator. The smoothly clipped absolute deviation (SCAD) ℓ1--penalty of Fan and Li (2001) is then introduced into the MMSQ objective function in order to achieve sparse estimation of the scaling matrix which is the major responsible for the curse of dimensionality problem. We extend the asymptotic theory and we show that the sparse-MMSQ estimator enjoys the oracle properties under mild regularity conditions. The method is illustrated and its effectiveness is tested using several synthetic datasets simulated from the Elliptical Stable distribution (ESD) for which alternative methods are recognised to perform poorly. The method is then applied to build a new network-based systemic risk measurement framework. The proposed methodology to build the network relies on a new systemic risk measure and on a parametric test of statistical dominance.
statistics  reinventing-the-wheel  how-is-this-not-constrained-symbolic-regression?  algorithms  models-and-modes  to-understand  inference 
november 2017 by Vaguery
[1703.04977] What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
There are two major types of uncertainty one can model. Aleatoric uncertainty captures noise inherent in the observations. On the other hand, epistemic uncertainty accounts for uncertainty in the model -- uncertainty which can be explained away given enough data. Traditionally it has been difficult to model epistemic uncertainty in computer vision, but with new Bayesian deep learning tools this is now possible. We study the benefits of modeling epistemic vs. aleatoric uncertainty in Bayesian deep learning models for vision tasks. For this we present a Bayesian deep learning framework combining input-dependent aleatoric uncertainty together with epistemic uncertainty. We study models under the framework with per-pixel semantic segmentation and depth regression tasks. Further, our explicit uncertainty formulation leads to new loss functions for these tasks, which can be interpreted as learned attenuation. This makes the loss more robust to noisy data, also giving new state-of-the-art results on segmentation and depth regression benchmarks.
computer-vision  machine-learning  models-and-modes  uncertainty  deep-learning  rather-interesting  define-your-terms  representation  nudge-targets  to-wrt 
may 2017 by Vaguery
[1312.7604] Probabilistic Archetypal Analysis
Archetypal analysis represents a set of observations as convex combinations of pure patterns, or archetypes. The original geometric formulation of finding archetypes by approximating the convex hull of the observations assumes them to be real valued. This, unfortunately, is not compatible with many practical situations. In this paper we revisit archetypal analysis from the basic principles, and propose a probabilistic framework that accommodates other observation types such as integers, binary, and probability vectors. We corroborate the proposed methodology with convincing real-world applications on finding archetypal winter tourists based on binary survey data, archetypal disaster-affected countries based on disaster count data, and document archetypes based on term-frequency data. We also present an appropriate visualization tool to summarize archetypal analysis solution better.
machine-learning  dimension-reduction  rather-interesting  models-and-modes  to-understand  to-write-about 
february 2017 by Vaguery
The Archdruid Report: Perched on the Wheel of Time
In the final chapters of his second volume, for example, Spengler noted that civilizations in the stage ours was about to reach always end up racked by conflicts that pit established hierarchies against upstart demagogues who rally the disaffected and transform them into a power base. Looking at the trends visible in his own time, he sketched out the most likely form those conflicts would take in the Winter phase of our civilization. Modern representative democracy, he pointed out, has no effective defenses against corruption by wealth, and so could be expected to evolve into corporate-bureaucratic plutocracies that benefit the affluent at the expense of everyone else. Those left out in the cold by these transformations, in turn, end up backing what Spengler called Caesarism—the rise of charismatic demagogues who challenge and eventually overturn the corporate-bureaucratic order.

These demagogues needn’t come from within the excluded classes, by the way. Julius Caesar, the obvious example, came from an old upper-class Roman family and parlayed his family connections into a successful political career. Watchers of the current political scene may be interested to know that Caesar during his lifetime wasn’t the imposing figure he became in retrospect; he had a high shrill voice, his morals were remarkably flexible even by Roman standards—the scurrilous gossip of his time called him “every man’s wife and every woman’s husband”—and he spent much of his career piling up huge debts and then wriggling out from under them. Yet he became the political standardbearer for the plebeian classes, and his assassination by a conspiracy of rich Senators launched the era of civil wars that ended the rule of the old elite once and for all.
history  political-economy  philosophy  models-and-modes  argumentation 
february 2017 by Vaguery
[1612.02483] High Dimensional Consistent Digital Segments
We consider the problem of digitalizing Euclidean line segments from ℝd to ℤd. Christ {\em et al.} (DCG, 2012) showed how to construct a set of {\em consistent digital segment} (CDS) for d=2: a collection of segments connecting any two points in ℤ2 that satisfies the natural extension of the Euclidean axioms to ℤd. In this paper we study the construction of CDSs in higher dimensions.
We show that any total order can be used to create a set of {\em consistent digital rays} CDR in ℤd (a set of rays emanating from a fixed point p that satisfies the extension of the Euclidean axioms). We fully characterize for which total orders the construction holds and study their Hausdorff distance, which in particular positively answers the question posed by Christ {\em et al.}.
approximation  computational-geometry  performance-measure  rather-interesting  mathematics  consistency  models-and-modes  constructive-geometry  nudge-targets  consider:representation  consider:looking-to-see 
january 2017 by Vaguery
[1508.05837] Hydroassets Portfolio Management for Intraday Electricity Trading in a Discrete Time Stochastic Optimization Perspective
Hydro storage system optimization is becoming one of the most challenging task in Energy Finance. Following the Blomvall and Lindberg (2002) interior point model, we set up a stochastic multiperiod optimization procedure by means of a "bushy" recombining tree that provides fast computational results. Inequality constraints are packed into the objective function by the logarithmic barrier approach and the utility function is approximated by its second order Taylor polynomial. The optimal solution for the original problem is obtained as a diagonal sequence where the first diagonal dimension is the parameter controlling the logarithmic penalty and the second is the parameter for the Newton step in the construction of the approximated solution. Optimal intraday electricity trading and water values for hydroassets as shadow prices are computed. The algorithm is implemented in Mathematica.
portfolio-theory  operations-research  financial-engineering  time-series  prediction  models-and-modes  nudge-targets  consider:performance-measures  consider:metaheuristics 
january 2017 by Vaguery

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