If you did not already know

If you did not already know

Time Stamp: November 5, 2023 1:19 PM
Source Node: 2973860

Semiotics google
Semiotics (also called semiotic studies) is the study of sign process (semiosis), which is any form of activity, conduct, or any process that involves signs, including the production of meaning. A sign is anything that communicates a meaning, that is not the sign itself, to the interpreter of the sign. The meaning can be intentional such as a word uttered with a specific meaning, or unintentional, such as a symptom being a sign of a particular medical condition. Signs can communicate through any of the senses, visual, auditory, tactile, olfactory, or taste. The semiotic tradition explores the study of signs and symbols as a significant part of communications. Unlike linguistics, semiotics also studies non-linguistic sign systems. Semiotics includes the study of signs and sign processes, indication, designation, likeness, analogy, allegory, metonymy, metaphor, symbolism, signification, and communication. Semiotics is frequently seen as having important anthropological and sociological dimensions; for example, the Italian semiotician and novelist Umberto Eco proposed that every cultural phenomenon may be studied as communication. Some semioticians focus on the logical dimensions of the science, however. They examine areas belonging also to the life sciences – such as how organisms make predictions about, and adapt to, their semiotic niche in the world (see semiosis). In general, semiotic theories take signs or sign systems as their object of study: the communication of information in living organisms is covered in biosemiotics (including zoosemiotics and phytosemiotics). Semiotics is not to be confused with the Saussurean tradition called semiology, which is a subset of semiotics. …

Complier Average Causal Effects (CACE) google
Typically, studies analyze data based on treatment assignment rather than treatment received. This focus on assignment is called an intention-to-treat (ITT) analysis. In a policy environment, the ITT may make a lot of sense; we are answering this specific question: ‘What is the overall effect in the real world where the intervention is made available yet some people take advantage of it while others do not?’ Alternatively, researchers may be interested in different question: ‘What is the causal effect of actually receiving the treatment?’ Now, to answer the second question, there are numerous subtle issues that you need to wrestle with (again, go take the course). But, long story short, we need to (1) identify the folks in the intervention group who actually do what they have been encouraged to do (receive the intervention) but only because they were encouraged, and not because they would have received the intervention anyways had they not been randomized, and compare their outcomes with (2) the folks in the control group who did not seek out the intervention on their own initiative but would have received the intervention had they been encouraged. These two groups are considered to be compliers – they would always do what they are told in the context of the study. And the effect of the intervention that is based on outcomes from this type of patient is called the complier average causal effect (CACE). …

Augmented Inverse Probability Weighting (AIPWT) google
In this paper, we discuss an estimator for average treatment effects (ATEs) known as the augmented inverse propensity weighted (AIPW) estimator. This estimator has attractive theoretical properties and only requires practitioners to do two things they are already comfortable with: (1) specify a binary regression model for the propensity score, and (2) specify a regression model for the outcome variable. Perhaps the most interesting property of this estimator is its so-called ‘‘double robustness.” Put simply, the estimator remains consistent for the ATE if either the propensity score model or the outcome regression is misspecified but the other is properly specified. After explaining the AIPW estimator, we conduct a Monte Carlo experiment that compares the finite sample performance of the AIPW estimator to three common competitors: a regression estimator, an inverse propensity weighted (IPW) estimator, and a propensity score matching estimator. The Monte Carlo results show that the AIPW estimator has comparable or lower mean square error than the competing estimators when the propensity score and outcome models are both properly specified and, when one of the models is misspecified, the AIPW estimator is superior.
‘Robust-squared’ Imputation Models Using BART …

FuzzerGym google
Fuzzing is a commonly used technique designed to test software by automatically crafting program inputs. Currently, the most successful fuzzing algorithms emphasize simple, low-overhead strategies with the ability to efficiently monitor program state during execution. Through compile-time instrumentation, these approaches have access to numerous aspects of program state including coverage, data flow, and heterogeneous fault detection and classification. However, existing approaches utilize blind random mutation strategies when generating test inputs. We present a different approach that uses this state information to optimize mutation operators using reinforcement learning (RL). By integrating OpenAI Gym with libFuzzer we are able to simultaneously leverage advancements in reinforcement learning as well as fuzzing to achieve deeper coverage across several varied benchmarks. Our technique connects the rich, efficient program monitors provided by LLVM Santizers with a deep neural net to learn mutation selection strategies directly from the input data. The cross-language, asynchronous architecture we developed enables us to apply any OpenAI Gym compatible deep reinforcement learning algorithm to any fuzzing problem with minimal slowdown. …

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