How training and testing histories affect generalization: a test of simple neural networks

We show that a simple network model of associative learning can\ud reproduce three findings that arise from particular training and\ud testing procedures in generalization experiments: the effect of 1)\ud ``errorless learning'' and 2) extinction testing on peak shift, and\ud 3) the central tendency effect. These findings provide a true test\ud of the network model, which was developed to account for other\ud penhomena, and highlight the potential of neural networks to study\ud phenomena that depend on sequences of experiences with many stimuli.\ud Our results suggest that at least some such phenomena, e.g.,\ud stimulus range effects, may derive from basic mechanisms of\ud associative memory rather than from more complex memory processes

From Both Sides Now: The Job Talk’s Role in Matching Candidates with Law Schools

In the heavily competitive law school teaching job market, the so-called “job talk” has assumed increasing importance in the ultimate hiring decision. Nevertheless, there is little published information to assist a law school faculty in structuring or evaluating the job talk and a similar paucity of information for candidates to guide them in creating and preparing for the presentation of their talk. This article is intended to fill that void. The article guides the preparation of faculty and candidates for both the job talk itself and for the crucial Q&A period that follows the talk. The article represents the authors’ collective 87 years of experience seeing both successful and unsuccessful job talks, as well as the experience of 15 colleagues around the country who reviewed our initial draft and commented on it from the perspective of their various law schools

From Both Sides Now: The Job Talk’s Role in Matching Candidates with Law Schools

In the heavily competitive law school teaching job market, the so-called “job talk” has assumed increasing importance in the ultimate hiring decision. Nevertheless, there is little published information to assist a law school faculty in structuring or evaluating the job talk and a similar paucity of information for candidates to guide them in creating and preparing for the presentation of their talk. This article is intended to fill that void. The article guides the preparation of faculty and candidates for both the job talk itself and for the crucial Q&A period that follows the talk. The article represents the authors’ collective 87 years of experience seeing both successful and unsuccessful job talks, as well as the experience of 15 colleagues around the country who reviewed our initial draft and commented on it from the perspective of their various law schools

On estimating the exponent of power-law frequency distributions

Power-law frequency distributions characterize a wide array of natural phenomena. In ecology, biology, and many physical and social sciences, the exponents of these power-laws are estimated to draw inference about the processes underlying the phenomenon, to test theoretical models, and to scale up from local observations to global patterns. Therefore, it is essential that these exponents be estimated accurately. Unfortunately, the binning-based methods traditionally utilized in ecology and other disciplines perform quite poorly. Here we discuss more sophisticated methods for fitting these exponents based on cumulative distribution functions and maximum likelihood estimation. We illustrate their superior performance at estimating known exponents and provide details on how and when ecologists should use them. Our results confirm that maximum likelihood estimation out-performs other methods in both accuracy and precision. Because of the use of biased statistical methods for estimating the exponent, the conclusions of several recently published papers should be revisited

Leonardo's rule, self-similarity and wind-induced stresses in trees

Examining botanical trees, Leonardo da Vinci noted that the total cross-section of branches is conserved across branching nodes. In this Letter, it is proposed that this rule is a consequence of the tree skeleton having a self-similar structure and the branch diameters being adjusted to resist wind-induced loads

Branching principles of animal and plant networks identified by combining extensive data, machine learning, and modeling

Branching in vascular networks and in overall organismic form is one of the most common and ancient features of multicellular plants, fungi, and animals. By combining machine-learning techniques with new theory that relates vascular form to metabolic function, we enable novel classification of diverse branching networks--mouse lung, human head and torso, angiosperm and gymnosperm plants. We find that ratios of limb radii--which dictate essential biologic functions related to resource transport and supply--are best at distinguishing branching networks. We also show how variation in vascular and branching geometry persists despite observing a convergent relationship across organisms for how metabolic rate depends on body mass.Comment: 55 pages, 8 figures, 8 table