Adversarial nets with perceptual losses for text-to-image synthesis

Recent approaches in generative adversarial networks (GANs) can automatically synthesize realistic images from descriptive text. Despite the overall fair quality, the generated images often expose visible flaws that lack structural definition for an object of interest. In this paper, we aim to extend state of the art for GAN-based text-to-image synthesis by improving perceptual quality of generated images. Differentiated from previous work, our synthetic image generator optimizes on perceptual loss functions that measure pixel, feature activation, and texture differences against a natural image. We present visually more compelling synthetic images of birds and flowers generated from text descriptions in comparison to some of the most prominent existing work

A Concise Total Synthesis of (--)-Maoecrystal Z

The first total synthesis of (--)-maoecrystal Z is described. The key steps of the synthesis include a diastereoselective Ti^(III)-mediated reductive epoxide coupling reaction and a diastereoselective Sm^(II)-mediated reductive cascade cyclization reaction. These transformations enabled the preparation of (--)-maoecrystal Z in only 12 steps from (--)-γ-cyclogeraniol

Multimodal Sparse Coding for Event Detection

Unsupervised feature learning methods have proven effective for classification tasks based on a single modality. We present multimodal sparse coding for learning feature representations shared across multiple modalities. The shared representations are applied to multimedia event detection (MED) and evaluated in comparison to unimodal counterparts, as well as other feature learning methods such as GMM supervectors and sparse RBM. We report the cross-validated classification accuracy and mean average precision of the MED system trained on features learned from our unimodal and multimodal settings for a subset of the TRECVID MED 2014 dataset.Comment: Multimodal Machine Learning Workshop at NIPS 201

Learning a Static Analyzer from Data

To be practically useful, modern static analyzers must precisely model the effect of both, statements in the programming language as well as frameworks used by the program under analysis. While important, manually addressing these challenges is difficult for at least two reasons: (i) the effects on the overall analysis can be non-trivial, and (ii) as the size and complexity of modern libraries increase, so is the number of cases the analysis must handle. In this paper we present a new, automated approach for creating static analyzers: instead of manually providing the various inference rules of the analyzer, the key idea is to learn these rules from a dataset of programs. Our method consists of two ingredients: (i) a synthesis algorithm capable of learning a candidate analyzer from a given dataset, and (ii) a counter-example guided learning procedure which generates new programs beyond those in the initial dataset, critical for discovering corner cases and ensuring the learned analysis generalizes to unseen programs. We implemented and instantiated our approach to the task of learning JavaScript static analysis rules for a subset of points-to analysis and for allocation sites analysis. These are challenging yet important problems that have received significant research attention. We show that our approach is effective: our system automatically discovered practical and useful inference rules for many cases that are tricky to manually identify and are missed by state-of-the-art, manually tuned analyzers

Batalin-Tyutin Quantization of the Chiral Schwinger Model

We quantize the chiral Schwinger Model by using the Batalin-Tyutin formalism. We show that one can systematically construct the first class constraints and the desired involutive Hamiltonian, which naturally generates all secondary constraints. For $a>1$, this Hamiltonian gives the gauge invariant Lagrangian including the well-known Wess-Zumino terms, while for $a=1$ the corresponding Lagrangian has the additional new type of the Wess-Zumino terms, which are irrelevant to the gauge symmetry.Comment: 15 pages, latex, no figures, to be published in Z. Phys. C (1995

Enhancing Equity in Public Transportation Using Geographic Information Systems and Spatial Optimization

Public transportation is a vital part of urban living. For instance, public transportation services help reduce road congestion, oil consumption and air pollution, and they serve people who need to travel throughout urban environments at the same time do not have access to private vehicles. The latter aspect is an important matter of social justice. Therefore, it is important to understand why the interest in equity in transport is growing, why public transportation should favor the transport disadvantaged, and why analyses of equity measurement and improvement are needed. Measuring the level of access to public transportation among the transport disadvantaged provides a theoretical basis for analyzing potential improvements in access by adjusting public transportation facility locations. This research will focus on modeling approaches used in establishing public transportation infrastructure and systems. Using GIS and spatial optimization models, the level of access to public transportation in terms of equity will be evaluated and improvement of the level of access will be attempted by offering new service stop locations. To this end, using the Maximal Covering Location Problem (MCLP), the optimal locations of potential facilities to cover equity favoring origin- and destination-based demand are identified. This research finally provides a set of optimal service stop locations maximizing coverage of origin- and destination-based demand simultaneously through implementation of a bi-objective model, applied to the City of Hilliard, Ohio

Flowers For The World: Developing a Business Game to Support the Teaching of IS Concepts

One of the key problems in teaching fundamental concepts in information systems is how to ground the theory in experiences that the students can relate to. To overcome this problem, a business game called Flowers For The World has been developed and used across a wide variety of IS courses. This paper will describe the game and the result of using it for a 300-level course in analysis and design. The possibility exists that the game could be developed to provide a common business foundation across all business school curricula

Osmotic potential, photosynthetic abilities and growth characters of oil palm (Elaeis guineensis Jacq.) seedlings in responses to polyethylene glycol-induced water deficit

The aim of the present study is to investigate the biochemical, physiological and morphological responses of oil palm seedlings when exposed to polyethylene glycol (PEG)-induced water deficit. Oil palm seedlings were photo-autotrophically grown in MS media and subsequently exposed to -0.23 (control), -0.42, -0.98 or -2.15 MPa PEG-induced water deficit. Osmotic potential (Ψs) in root and leaf tissues of oil palm seedlings grown under PEG-induced water deficit was decreased leading to chlorophyll degradation. Chlorophyll a (Chla), chlorophyll b (Chlb), total chlorophyll (TC), total carotenoids (Cx+c), maximum quantum yield of photosystem II (PSII) (Fv/Fm) and photon yield of PSII (ΦPSII) in the oil palm seedlings under water deficit conditions dropped significantly in comparison to the control group, leading to a reduction in net-photosynthetic rate (Pn) and growth. A positive correlation between physiological and growth parameters, including osmotic potential, photosynthetic pigments and water oxidation in photosystem II and Pn was demonstrated. These data provide the basis for the establishment of multivariate criteria for water deficit tolerance screening in oil palm breeding programs.Key words: Chlorophyll fluorescence, net-photosynthetic rate, pigment, water oxidation, water deficit stress