3,512 research outputs found
The Anatomy of a Spin-Off
Section 355 of the Internal Revenue Code, which specially treats certain forms of corporate reorganization, has been the constant subject of both scholarly commentary and litigation. The experience of functioning under these spin-off provisions has resulted in clarification of the law and has also raised significant questions as to its scope. The precise limits of the section remain to some extent in a state of flux and await further exposition by the courts
Optimal integration of texture and motion cues to depth
AbstractWe report the results of a depth-matching experiment in which subjects were asked to adjust the height of an ellipse until it matched the depth of a simulated cylinder defined by texture and motion cues. In one-third of the trials the shape of the cylinder was primarily given by motion information, in another one-third of the trials it was given by texture information, and on the remaining trials it was given by both sources of information. Two optimal cue combination models are described where optimality is defined in terms of Bayesian statistics. The parameter values of the models are set based on subjects’ responses on trials when either the motion cue or the texture cue was informative. These models provide predictions of subjects’ responses on trials when both cues were informative. The results indicate that one of the optimal models provides a good fit to the subjects’ data, and the second model provides an exceptional fit. Because the predictions of the optimal models closely match the experimental data, we conclude that observers’ cue-combination strategies are indeed optimal, at least under the conditions studied here
Targeted genome modifications in soybean with CRISPR/Cas9
Background: The ability to selectively alter genomic DNA sequences in vivo is a powerful tool for basic and applied research. The CRISPR/Cas9 system precisely mutates DNA sequences in a number of organisms. Here, the CRISPR/Cas9 system is shown to be effective in soybean by knocking-out a green fluorescent protein (GFP) transgene and modifying nine endogenous loci.
Results: Targeted DNA mutations were detected in 95% of 88 hairy-root transgenic events analyzed. Bi-allelic mutations were detected in events transformed with eight of the nine targeting vectors. Small deletions were the most common type of mutation produced, although SNPs and short insertions were also observed. Homoeologous genes were successfully targeted singly and together, demonstrating that CRISPR/Cas9 can both selectively, and generally, target members of gene families. Somatic embryo cultures were also modified to enable the production of plants with heritable mutations, with the frequency of DNA modifications increasing with culture time. A novel cloning strategy and vector system based on In-Fusion (R) cloning was developed to simplify the production of CRISPR/Cas9 targeting vectors, which should be applicable for targeting any gene in any organism.
Conclusions: The CRISPR/Cas9 is a simple, efficient, and highly specific genome editing tool in soybean. Although some vectors are more efficient than others, it is possible to edit duplicated genes relatively easily. The vectors and methods developed here will be useful for the application of CRISPR/Cas9 to soybean and other plant species
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A 3D shape inference model matches human visual object similarity judgmentsbetter than deep convolutional neural networks
In the past few years, deep convolutional neural networks(CNNs) trained on large image data sets have shown impres-sive visual object recognition performances. Consequently,these models have attracted the attention of the cognitive sci-ence community. Recent studies comparing CNNs with neuraldata from cortical area IT suggest that CNNs may—in addi-tion to providing good engineering solutions—provide goodmodels of biological visual systems. Here, we report evidencethat CNNs are, in fact, not good models of human visual per-ception. We show that a 3D shape inference model explainshuman performance on an object shape similarity task betterthan CNNs. We argue that deep neural networks trained onlarge amounts of image data to maximize object recognitionperformance do not provide adequate models of human vision
Hierarchical Mixtures of Experts and the EM Algorithm
We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain
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Efficient Data Compression Leads to Categorical Bias inPerception and Perceptual Memory
Efficient data compression is essential for capacity-limited sys-tems, such as biological memory. We hypothesize that the needfor efficient data compression shapes biological perception andperceptual memory in many of the same ways that it shapesengineered systems. If true, then the tools that engineers useto analyze and design systems, namely rate-distortion theory(RDT), can profitably be used to understand perception andmemory. To date, researchers have used deep neural networksto approximately implement RDT in high-dimensional spaces,but these implementations have been limited to tasks in whichthe sole goal is compression with respect to reconstruction er-ror. Here, we introduce a new deep neural network architecturethat approximately implements RDT in a task-general manner.An important property of our architecture is that it is trained“end-to-end”, operating on raw perceptual input (e.g., pixels)rather than an intermediate level of abstraction, as is the casewith most psychological models. We demonstrate that ourframework can mimick categorical biases in perception andperceptual memory in several ways, and thus generates spe-cific hypotheses that can be tested empirically in future work
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