Notes

Affiliation in between 16S rRNA gene versions along with the likelihood of amikacin in Mycobacterium avium Complex and also Mycobacterium abscessus clinical isolates.
The recent turn to "big data" from social media corpora has enabled sociolinguists to investigate patterns of language variation and change at unprecedented scales. However, research in this paradigm has been slow to address variable phenomena in minority languages, where data scarcity and the absence of computational tools (e.g., taggers, parsers) often present significant barriers to entry. This article analyzes socio-syntactic variation in one minority language variety, Hasidic Yiddish, focusing on a variable for which tokens can be identified in raw text using purely morphological criteria. In non-finite particle verbs, the overt tense marker tsu (cf. English to, German zu) is variably realized either between the preverbal particle and verb (e.g., oyf-tsu-es-n up-to-eat-INF 'to eat up'; the conservative variant) or before both elements (tsu oyf-es-n to up-eat-INF; the innovative variant). Nearly 38,000 tokens of non-finite particle verbs were extracted from the popular Hasidic Yiddish discussion forum Kave Shtiebel (the 'coffee room'; kaveshtiebel.com). A mixed-effects regression analysis reveals that despite a forum-wide favoring effect for the innovative variant, users favor the conservative variant the longer their accounts remain open and active. This process of rapid implicit standardization is supported by ethnographic evidence highlighting the spread of language norms among Hasidic writers on the internet, most of whom did not have the opportunity to express themselves in written Yiddish prior to the advent of social media.Recent work on fairness in machine learning has primarily emphasized how to define, quantify, and encourage "fair" outcomes. Less attention has been paid, however, to the ethical foundations which underlie such efforts. Among the ethical perspectives that should be taken into consideration is consequentialism, the position that, roughly speaking, outcomes are all that matter. Although consequentialism is not free from difficulties, and although it does not necessarily provide a tractable way of choosing actions (because of the combined problems of uncertainty, subjectivity, and aggregation), it nevertheless provides a powerful foundation from which to critique the existing literature on machine learning fairness. Moreover, it brings to the fore some of the tradeoffs involved, including the problem of who counts, the pros and cons of using a policy, and the relative value of the distant future. In this paper we provide a consequentialist critique of common definitions of fairness within machine learning, as well as a machine learning perspective on consequentialism. We conclude with a broader discussion of the issues of learning and randomization, which have important implications for the ethics of automated decision making systems.The issue of fairness in machine learning models has recently attracted a lot of attention as ensuring it will ensure continued confidence of the general public in the deployment of machine learning systems. We focus on mitigating the harm incurred by a biased machine learning system that offers better outputs (e.g., loans, job interviews) for certain groups than for others. https://www.selleckchem.com/products/e-7386.html We show that bias in the output can naturally be controlled in probabilistic models by introducing a latent target output. This formulation has several advantages first, it is a unified framework for several notions of group fairness such as Demographic Parity and Equality of Opportunity; second, it is expressed as a marginalization instead of a constrained problem; and third, it allows the encoding of our knowledge of what unbiased outputs should be. Practically, the second allows us to avoid unstable constrained optimization procedures and to reuse off-the-shelf toolboxes. The latter translates to the ability to control the level of fairness by directly varying fairness target rates. In contrast, existing approaches rely on intermediate, arguably unintuitive, control parameters such as covariance thresholds.Evaluating information access tasks, including textual and multimedia search, question answering, and understanding has been the core mission of NIST's Retrieval Group since 1989. The TRECVID Evaluations of Multimedia Access began in 2001 with a goal of driving content-based search technology for multimedia just as its progenitor, the Text Retrieval Conference (TREC) did for text and web.The Free Energy Principle and Active Inference Framework (FEP-AI) begins with the understanding that persisting systems must regulate environmental exchanges and prevent entropic accumulation. In FEP-AI, minds and brains are predictive controllers for autonomous systems, where action-driven perception is realized as probabilistic inference. Integrated Information Theory (IIT) begins with considering the preconditions for a system to intrinsically exist, as well as axioms regarding the nature of consciousness. IIT has produced controversy because of its surprising entailments quasi-panpsychism; subjectivity without referents or dynamics; and the possibility of fully-intelligent-yet-unconscious brain simulations. Here, I describe how these controversies might be resolved by integrating IIT with FEP-AI, where integrated information only entails consciousness for systems with perspectival reference frames capable of generating models with spatial, temporal, and causal coherence for self and world. Without that coriori estimates as coherent vectors governing neural evolution, with alpha frequencies generating basic awareness, and cross-frequency phase-coupling within theta frequencies for access consciousness and volitional control. These dynamic cores of integrated information also function as global workspaces, centered on posterior cortices, but capable of being entrained with frontal cortices and interoceptive hierarchies, thus affording agentic causation. Integrated World Modeling Theory (IWMT) represents a synthetic approach to understanding minds that reveals compatibility between leading theories of consciousness, thus enabling inferential synergy.This paper illuminates some root causes of confusion about dynamic representation in music technology and introduces a system that addresses this problem to provide context-dependent dynamics for machine learning-aided performance. While terms used for dynamic representations like forte and mezzo-forte have been extant for centuries, the canon gives us no straight answer on how these terms must be applied to literal decibel ranges. The common conception that dynamic terms should be understood as context-dependent is ubiquitous and reasonably simple for most human musicians to grasp. This logic breaks down when applied to digital music technologies. At a fundamental level, these technologies define all musical parameters using discrete numbers, rather than with continuous data, making it impossible for these technologies to make context-dependent decisions. The authors give examples in which this lack of contextual inputs in music technology often leads musicians, composers, and producers to ignore dynamics altogether as a concern in their given practice.
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