DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets

We propose DefogGAN, a generative approach to the problem of inferring state information hidden in the fog of war for real-time strategy (RTS) games. Given a partially observed state, DefogGAN generates defogged images of a game as predictive information. Such information can lead to create a strategic agent for the game. DefogGAN is a conditional GAN variant featuring pyramidal reconstruction loss to optimize on multiple feature resolution scales.We have validated DefogGAN empirically using a large dataset of professional StarCraft replays. Our results indicate that DefogGAN can predict the enemy buildings and combat units as accurately as professional players do and achieves a superior performance among state-of-the-art defoggers

Is Cross-modal Information Retrieval Possible without Training?

Encoded representations from a pretrained deep learning model (e.g., BERT text embeddings, penultimate CNN layer activations of an image) convey a rich set of features beneficial for information retrieval. Embeddings for a particular modality of data occupy a high-dimensional space of its own, but it can be semantically aligned to another by a simple mapping without training a deep neural net. In this paper, we take a simple mapping computed from the least squares and singular value decomposition (SVD) for a solution to the Procrustes problem to serve a means to cross-modal information retrieval. That is, given information in one modality such as text, the mapping helps us locate a semantically equivalent data item in another modality such as image. Using off-the-shelf pretrained deep learning models, we have experimented the aforementioned simple cross-modal mappings in tasks of text-to-image and image-to-text retrieval. Despite simplicity, our mappings perform reasonably well reaching the highest accuracy of 77% on recall@10, which is comparable to those requiring costly neural net training and fine-tuning. We have improved the simple mappings by contrastive learning on the pretrained models. Contrastive learning can be thought as properly biasing the pretrained encoders to enhance the cross-modal mapping quality. We have further improved the performance by multilayer perceptron with gating (gMLP), a simple neural architecture

Deep u*- and g-band Imaging of the Spitzer Space Telescope First Look Survey Field : Observations and Source Catalogs

We present deep u*-, and g-band images taken with the MegaCam on the 3.6 m Canada-France-Hawaii Telescope (CFHT) to support the extragalactic component of the Spitzer First Look Survey (hereafter, FLS). In this paper we outline the observations, present source catalogs and characterize the completeness, reliability, astrometric accuracy and number counts of this dataset. In the central 1 deg2 region of the FLS, we reach depths of g~26.5 mag, and u*~26.2 mag (AB magnitude, 5$\sigma$ detection over a 3" aperture) with ~4 hours of exposure time for each filter. For the entire FLS region (~5 deg2 coverage), we obtained u*-band images to the shallower depth of u*=25.0--25.4 mag (5$\sigma$, 3" aperture). The average seeing of the observations is 0.85" for the central field, and ~1.00" for the other fields. Astrometric calibration of the fields yields an absolute astrometric accuracy of 0.15" when matched with the SDSS point sources between 18<g<22. Source catalogs have been created using SExtractor. The catalogs are 50% complete and greater than 99.3% reliable down to g~26.5 mag and u*~26.2 mag for the central 1 deg2 field. In the shallower u*-band images, the catalogs are 50% complete and 98.2% reliable down to 24.8--25.4 mag. These images and source catalogs will serve as a useful resource for studying the galaxy evolution using the FLS data.Comment: 15 pages, 16 figure

Caprylate production with lactate as electron donor using Megasphaera hexanoica

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Massive Lyman Break Galaxies at z~3 in the Spitzer Extragalactic First Look Survey

We investigate the properties of 1088 Lyman Break Galaxies (LBGs) at z~3 selected from a ~2.63$deg2 sub-region of the First Look Survey field using the ground-based multi-color data and the Spitzer Space Telescope mid-infrared data at 3--8 and 24 um. With the wide area and the broad wavelength coverage, we sample a large number of ``rare'' u-band dropouts which are massive (M* > 10^11 Msun), allowing us to perform a statistical analysis of these subsets of LBGs that have not been studied in detail. Optically bright (R(AB) < 24.5 mag) LBGs detected in mid-infrared (S_{3.6um} > 6 uJy) reside at the most massive and dusty end of the LBG population, with relatively high and tight$M/L$ in rest-frame near-infrared. Most infrared-luminous LBGs (S_{24um} > 100 uJy) are dusty star-forming galaxies with star formation rates of 100--1000 Msun/yr, total infrared luminosity of > 10^12 Lsun. By constructing the UV luminosity function of massive LBGs, we estimate that the lower limit for the star formation rate density from LBGs more massive than 10^11 Msun at z~3 is > 3.3 x 10^-3 Msun/yr/Mpc^3, showing for the first time that the UV-bright population of massive galaxies alone contributes significantly to the global star formation rate density at z~3. When combined with the star formation rate densities at z < 2, our result reveals a steady increase in the contribution of massive galaxies to the global star formation from z=0 to z=3, providing strong support to the downsizing of galaxy formation.Comment: 15 pages, 13 figures. Accepted for publication in Ap