Hasil untuk "Law"

T. Tyler

People obey the law if they believe it's legitimate, not because they fear punishment--this is the startling conclusion of Tom Tyler's classic study. Tyler suggests that lawmakers and law enforcers would do much better to make legal systems worthy of respect than to try to instill fear of punishment. He finds that people obey law primarily because they believe in respecting legitimate authority. In his fascinating new afterword, Tyler brings his book up to date by reporting on new research into the relative importance of legal legitimacy and deterrence, and reflects on changes in his own thinking since his book was first published.

J. Habermas, William Rehg

Translatora s Introduction. Preface. 1. Law as a Category of Social Mediation between Facts and Norms. 2. The Sociology of Law versus the Philosophy of Justice. 3. A Reconstructive Approach to Law I: The System of Rights. 4. A Reconstructive Approach to Law II: The Principles of the Constitutional State. 5. The Indeterminacy of Law and the Rationality of Adjudication. 6. Judiciary and Legislature: On the Role and Legitimacy of Constitutional Adjudication. 7. Deliberative Politics: A Procedural Concept of Democracy. 8. Civil Society and the Political Public Sphere. 9. Paradigms of Law. Postscript (1994). Appendices. Notes. Bibliography. Index.

M. Waldrop

R. Sander

Abstract. Many atmospheric chemicals occur in the gas phase as well as in liquid cloud droplets and aerosol particles. Therefore, it is necessary to understand their distribution between the phases. According to Henry’s law, the equilibrium ratio between the abundances in the gas phase and in the aqueous phase is constant for a dilute solution. Henry’s law constants of trace gases of potential importance in environmental chemistry have been collected and converted into a uniform format. The compilation contains 46 434 values of Henry’s law constants for 10 173 species, collected from 995 references. It is also available on the internet at https://www.henrys-law.org (last access: October 2023). This article is a living review that supersedes the now obsolete publication by Sander (2015).

M. Fineman, Anna Grear

Ben Sorscher, Robert Geirhos, Shashank Shekhar et al.

Widely observed neural scaling laws, in which error falls off as a power of the training set size, model size, or both, have driven substantial performance improvements in deep learning. However, these improvements through scaling alone require considerable costs in compute and energy. Here we focus on the scaling of error with dataset size and show how in theory we can break beyond power law scaling and potentially even reduce it to exponential scaling instead if we have access to a high-quality data pruning metric that ranks the order in which training examples should be discarded to achieve any pruned dataset size. We then test this improved scaling prediction with pruned dataset size empirically, and indeed observe better than power law scaling in practice on ResNets trained on CIFAR-10, SVHN, and ImageNet. Next, given the importance of finding high-quality pruning metrics, we perform the first large-scale benchmarking study of ten different data pruning metrics on ImageNet. We find most existing high performing metrics scale poorly to ImageNet, while the best are computationally intensive and require labels for every image. We therefore developed a new simple, cheap and scalable self-supervised pruning metric that demonstrates comparable performance to the best supervised metrics. Overall, our work suggests that the discovery of good data-pruning metrics may provide a viable path forward to substantially improved neural scaling laws, thereby reducing the resource costs of modern deep learning.

Jonathan H. Choi, Kristin E. Hickman, Amy B. Monahan et al.

How well can AI models write law school exams without human assistance? To find out

A. Clauset, C. Shalizi, M. Newman

Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution—the part of the distribution representing large but rare events—and by the difficulty of identifying the range over which power-law behavior holds. Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov (KS) statistic and likelihood ratios. We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches. We also apply the proposed methods to twenty-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data, while in others the power law is ruled out.

R. Porta, Florencio Lopez‐de‐Silanes, A. Shleifer et al.

M. Newman

J. Kennicutt, J. Kennicutt

Measurements of Hα, H I, and CO distributions in 61 normal spiral galaxies are combined with published far-infrared and CO observations of 36 infrared-selected starburst galaxies, in order to study the form of the global star formation law over the full range of gas densities and star formation rates (SFRs) observed in galaxies. The disk-averaged SFRs and gas densities for the combined sample are well represented by a Schmidt law with index N = 1.4 ± 0.15. The Schmidt law provides a surprisingly tight parametrization of the global star formation law, extending over several orders of magnitude in SFR and gas density. An alternative formulation of the star formation law, in which the SFR is presumed to scale with the ratio of the gas density to the average orbital timescale, also fits the data very well. Both descriptions provide potentially useful "recipes" for modeling the SFR in numerical simulations of galaxy formation and evolution.

Franklin Allen, J. Qian, Meijun Qian

M. Faloutsos, P. Faloutsos, C. Faloutsos

B. Sweetman

L. Thurstone

W. Nix, Huajian Gao

Abstract We show that the indentation size effect for crystalline materials can be accurately modeled using the concept of geometrically necessary dislocations. The model leads to the following characteristic form for the depth dependence of the hardness: H H 0 1+ h ∗ h where H is the hardness for a given depth of indentation, h, H0 is the hardness in the limit of infinite depth and h ∗ is a characteristic length that depends on the shape of the indenter, the shear modulus and H0. Indentation experiments on annealed (111) copper single crystals and cold worked polycrystalline copper show that this relation is well-obeyed. We also show that this relation describes the indentation size effect observed for single crystals of silver. We use this model to derive the following law for strain gradient plasticity: ( σ σ 0 ) 2 = 1 + l χ , where σ is the effective flow stress in the presence of a gradient, σ0 is the flow stress in the absence of a gradient, χ is the effective strain gradient and l a characteristic material length scale, which is, in turn, related to the flow stress of the material in the absence of a strain gradient, l ≈ b( μ σ 0 ) 2 . For materials characterized by the power law σ 0 = σ ref e 1 n , the above law can be recast in a form with a strain-independent material length scale l. ( builtσ σ ref ) 2 = e 2 n + l χ l = b( μ σ ref ) 2 = l ( σ 0 σ ref ) 2 . This law resembles the phenomenological law developed by Fleck and Hutchinson, with their phenomenological length scale interpreted in terms of measurable material parametersbl].

Thomas D. Taylor

J. Habermas

L. Valen

G. Eysenbach

In an ongoing effort of this Journal to develop and further the theories, models, and best practices around eHealth research, this paper argues for the need for a “science of attrition”, that is, a need to develop models for discontinuation of eHealth applications and the related phenomenon of participants dropping out of eHealth trials. What I call “law of attrition” here is the observation that in any eHealth trial a substantial proportion of users drop out before completion or stop using the appplication. This feature of eHealth trials is a distinct characteristic compared to, for example, drug trials. The traditional clinical trial and evidence-based medicine paradigm stipulates that high dropout rates make trials less believable. Consequently eHealth researchers tend to gloss over high dropout rates, or not to publish their study results at all, as they see their studies as failures. However, for many eHealth trials, in particular those conducted on the Internet and in particular with self-help applications, high dropout rates may be a natural and typical feature. Usage metrics and determinants of attrition should be highlighted, measured, analyzed, and discussed. This also includes analyzing and reporting the characteristics of the subpopulation for which the application eventually “works”, ie, those who stay in the trial and use it. For the question of what works and what does not, such attrition measures are as important to report as pure efficacy measures from intention-to-treat (ITT) analyses. In cases of high dropout rates efficacy measures underestimate the impact of an application on a population which continues to use it. Methods of analyzing attrition curves can be drawn from survival analysis methods, eg, the Kaplan-Meier analysis and proportional hazards regression analysis (Cox model). Measures to be reported include the relative risk of dropping out or of stopping the use of an application, as well as a “usage half-life”, and models reporting demographic and other factors predicting usage discontinuation in a population. Differential dropout or usage rates between two interventions could be a standard metric for the “usability efficacy” of a system. A “run-in and withdrawal” trial design is suggested as a methodological innovation for Internet-based trials with a high number of initial dropouts/nonusers and a stable group of hardcore users.