Freedom from Violence and Lies

Freedom from Violence and Lies is a collection of forty-one essays by Simon Karlinsky (1924–2009), a prolific and controversial scholar of modern Russian literature, sexual politics, and music who taught in the University of California, Berkeley’s Department of Slavic Languages and Literatures from 1964 to 1991. Among Karlinsky’s full-length works are major studies of Marina Tsvetaeva and Nikolai Gogol, Russian Drama from Its Beginnings to the Age of Pushkin; editions of Anton Chekhov’s letters; writings by Russian émigrés; and correspondence between Vladimir Nabokov and Edmund Wilson. Karlinsky also wrote frequently for professional journals and mainstream publications like the New York Times Book Review and the Nation. The present volume is the first collection of such shorter writings, spanning more than three decades. It includes twenty-seven essays on literary topics and fourteen on music, seven of which have been newly translated from the Russian originals

Automation, Decision Making and Business to Business Pricing

In a world going towards automation, I ask whether salespeople making pricing decisions in a high human interaction environment such as business to business (B2B) retail, could be automated, and under what conditions it would be most beneficial. I propose a hybrid approach to automation that combines the expert salesperson and an artificial intelligence model of the salesperson in making pricing decisions in B2B. The hybrid approach preserves individual and organizational knowledge both by learning the expert's decision making behavior and by keeping the expert in the decision making process for decisions that require human judgment. Using sales transactions data from a B2B aluminum retailer, I create an automated version of each salesperson, that learns the salesperson's pricing policy based on her past pricing decisions. In a field experiment, I provide salespeople in the B2B retailer with their own model's price recommendations through their CRM system in real-time, and allow them to adjust their original pricing accordingly. I find that despite the loss of non-codeable information that is available to the salesperson but not to the model, providing the model's price increases profits for treated quotes by as much as 10% relative to a control condition, which translates to approximately $1.3 million in yearly profits. Using a counterfactual analysis, I also find that a hybrid pricing approach, that follows the model's pricing most of time, but defers to the salesperson's pricing when the model is missing important information is more profitable than pure automation or pure reliance on the salesperson's pricing. I find that in most cases the model's scalability and consistency lead to better pricing decisions that translate to higher profits, but when pricing uncommon products or pricing for unfamiliar clients it is best to use human judgment. I investigate different ways, including machine learning methods, to model the salesperson's behavior and to combine salespeople's expertise as reflected by their automated representations, and discuss implications for automation of tasks that involve soft skills

Human Pose Estimation using Deep Consensus Voting

In this paper we consider the problem of human pose estimation from a single still image. We propose a novel approach where each location in the image votes for the position of each keypoint using a convolutional neural net. The voting scheme allows us to utilize information from the whole image, rather than rely on a sparse set of keypoint locations. Using dense, multi-target votes, not only produces good keypoint predictions, but also enables us to compute image-dependent joint keypoint probabilities by looking at consensus voting. This differs from most previous methods where joint probabilities are learned from relative keypoint locations and are independent of the image. We finally combine the keypoints votes and joint probabilities in order to identify the optimal pose configuration. We show our competitive performance on the MPII Human Pose and Leeds Sports Pose datasets

Co-regularized Alignment for Unsupervised Domain Adaptation

Deep neural networks, trained with large amount of labeled data, can fail to generalize well when tested with examples from a \emph{target domain} whose distribution differs from the training data distribution, referred as the \emph{source domain}. It can be expensive or even infeasible to obtain required amount of labeled data in all possible domains. Unsupervised domain adaptation sets out to address this problem, aiming to learn a good predictive model for the target domain using labeled examples from the source domain but only unlabeled examples from the target domain. Domain alignment approaches this problem by matching the source and target feature distributions, and has been used as a key component in many state-of-the-art domain adaptation methods. However, matching the marginal feature distributions does not guarantee that the corresponding class conditional distributions will be aligned across the two domains. We propose co-regularized domain alignment for unsupervised domain adaptation, which constructs multiple diverse feature spaces and aligns source and target distributions in each of them individually, while encouraging that alignments agree with each other with regard to the class predictions on the unlabeled target examples. The proposed method is generic and can be used to improve any domain adaptation method which uses domain alignment. We instantiate it in the context of a recent state-of-the-art method and observe that it provides significant performance improvements on several domain adaptation benchmarks.Comment: NIPS 2018 accepted versio

Self-Supervised Classification Network

We present Self-Classifier -- a novel self-supervised end-to-end classification learning approach. Self-Classifier learns labels and representations simultaneously in a single-stage end-to-end manner by optimizing for same-class prediction of two augmented views of the same sample. To guarantee non-degenerate solutions (i.e., solutions where all labels are assigned to the same class) we propose a mathematically motivated variant of the cross-entropy loss that has a uniform prior asserted on the predicted labels. In our theoretical analysis we prove that degenerate solutions are not in the set of optimal solutions of our approach. Self-Classifier is simple to implement and scalable. Unlike other popular unsupervised classification and contrastive representation learning approaches, it does not require any form of pre-training, expectation maximization, pseudo-labelling, external clustering, a second network, stop-gradient operation or negative pairs. Despite its simplicity, our approach sets a new state of the art for unsupervised classification of ImageNet; and even achieves comparable to state-of-the-art results for unsupervised representation learning. Code: https://github.com/elad-amrani/self-classifierComment: Update method and add experiment

Tax Complexity and Small Business: A Comparison of the Perceptions of Tax Agents in the United States and Australia

There is ongoing pressure in both the United States and Australia to simplify their respective tax systems, particularly in regard to small business taxpayers. In the case of both regimes, if substantial progress is to be made towards simplification, the areas of greatest need and the necessary reforms will require careful evaluation. The views of tax agents (practitioners) are highly relevant to the implementation of successful reform in that both regimes rely on self-assessment. It was considered that by undertaking a cross-jurisdictional comparison a greater understanding of complexity, from the perspective of tax agents, could be gained and that, the consideration of alternate treatments could better inform tax policymakers. That is, what can we learn from each other? The article; compares and contrasts the perceptions of practitioners on small business tax complexity based on a questionnaire instrument conducted in the US and an electronic survey and case study conducted in Australia. Tax practitioners in the US consistently rated the areas of partnerships, estate, and gift valuations, tax deferred exchanges, frequency of law changes and retirement plans as the most complex and progressive tax rates, estimated taxes, social security/self-employment taxes, corporate capital gain provisions and cash v accrual method as the least complex. In comparison, Australian practitioners found the frequency of change, the volume of legislative material and the effect of change on other aspects of taxation (including reporting) to be the major causes of complexity. Capital gains tax provisions were regarded as complex as were self-managed superannuation funds and trusts, but similar to US tax practitioners, Australian tax agents did not find the use of tax rates or accounting methods to be complex. The policy implications of these findings are discussed for both regimes, including the implications of having small business-specific rules. Reprinted by permission of the publisher