Understanding the Effect of the Long Tail on Neural Network Compression

Network compression is now a mature sub-field of neural network research: over the last decade, significant progress has been made towards reducing the size of models and speeding up inference, while maintaining the classification accuracy. However, many works have observed that focusing on just the overall accuracy can be misguided. E.g., it has been shown that mismatches between the full and compressed models can be biased towards under-represented classes. This raises the important research question, \emph{can we achieve network compression while maintaining ``semantic equivalence'' with the original network?} In this work, we study this question in the context of the ``long tail'' phenomenon in computer vision datasets observed by Feldman, et al. They argue that \emph{memorization} of certain inputs (appropriately defined) is essential to achieving good generalization. As compression limits the capacity of a network (and hence also its ability to memorize), we study the question: are mismatches between the full and compressed models correlated with the memorized training data? We present positive evidence in this direction for image classification tasks, by considering different base architectures and compression schemes

The isopropylation of naphthalene with propene over H-mordenite: The catalysis at the internal and external acid sites

The isopropylation of naphthalene (NP) with propene over H-Mordenite (MOR) was studied under a wide range of reaction parameters: temperature, propene pressure, period, and NP/MOR ratio. Selective formation of 2,6-diisopropylnaphthalene (2,6-DIPN) was observed at reaction conditions, such as at low reaction temperature, under high propene pressure, and/or with high NP/MOR ratio. However, the decrease in the selectivities for 2,6-DIPN was observed at reaction conditions such as at high temperature, under low propene pressure, and/or with low NP/MOR ratio. The selectivities for 2,6-DIPN in the encapsulated products were remained high and constant under all reaction conditions. These results indicate that the selective formation of 2,6-DIPN occurs through the least bulky transition state due to the exclusion of the bulky isomers by the MOR channels. The decrease in the selectivities for 2,6-DIPN are due to the isomerization of 2,6-DIPN to 2,7-DIPN at the external acid sites, directing towards thermodynamic equilibrium of DIPN isomers

Accelerated Encrypted Execution of General-Purpose Applications

Fully Homomorphic Encryption (FHE) is a cryptographic method that guarantees the privacy and security of user data during computation. FHE algorithms can perform unlimited arithmetic computations directly on encrypted data without decrypting it. Thus, even when processed by untrusted systems, confidential data is never exposed. In this work, we develop new techniques for accelerated encrypted execution and demonstrate the significant performance advantages of our approach. Our current focus is the Fully Homomorphic Encryption over the Torus (CGGI) scheme, which is a current state-of-the-art method for evaluating arbitrary functions in the encrypted domain. CGGI represents a computation as a graph of homomorphic logic gates and each individual bit of the plaintext is transformed into a polynomial in the encrypted domain. Arithmetic on such data becomes very expensive: operations on bits become operations on entire polynomials. Therefore, evaluating even relatively simple nonlinear functions, such as a sigmoid, can take thousands of seconds on a single CPU thread. Using our novel framework for end-to-end accelerated encrypted execution called ArctyrEX, developers with no knowledge of complex FHE libraries can simply describe their computation as a C program that is evaluated over 40x faster on an NVIDIA DGX A100 and 6x faster with a single A100 relative to a 256-threaded CPU baseline

CosmoDC2: A Synthetic Sky Catalog for Dark Energy Science with LSST

This paper introduces cosmoDC2, a large synthetic galaxy catalog designed to support precision dark energy science with the Large Synoptic Survey Telescope (LSST). CosmoDC2 is the starting point for the second data challenge (DC2) carried out by the LSST Dark Energy Science Collaboration (LSST DESC). The catalog is based on a trillion-particle, 4.225 Gpc^3 box cosmological N-body simulation, the `Outer Rim' run. It covers 440 deg^2 of sky area to a redshift of z=3 and is complete to a magnitude depth of 28 in the r-band. Each galaxy is characterized by a multitude of properties including stellar mass, morphology, spectral energy distributions, broadband filter magnitudes, host halo information and weak lensing shear. The size and complexity of cosmoDC2 requires an efficient catalog generation methodology; our approach is based on a new hybrid technique that combines data-driven empirical approaches with semi-analytic galaxy modeling. A wide range of observation-based validation tests has been implemented to ensure that cosmoDC2 enables the science goals of the planned LSST DESC DC2 analyses. This paper also represents the official release of the cosmoDC2 data set, including an efficient reader that facilitates interaction with the data

The Dark Energy Survey : more than dark energy – an overview

This overview paper describes the legacy prospect and discovery potential of the Dark Energy Survey (DES) beyond cosmological studies, illustrating it with examples from the DES early data. DES is using a wide-field camera (DECam) on the 4 m Blanco Telescope in Chile to image 5000 sq deg of the sky in five filters (grizY). By its completion, the survey is expected to have generated a catalogue of 300 million galaxies with photometric redshifts and 100 million stars. In addition, a time-domain survey search over 27 sq deg is expected to yield a sample of thousands of Type Ia supernovae and other transients. The main goals of DES are to characterize dark energy and dark matter, and to test alternative models of gravity; these goals will be pursued by studying large-scale structure, cluster counts, weak gravitational lensing and Type Ia supernovae. However, DES also provides a rich data set which allows us to study many other aspects of astrophysics. In this paper, we focus on additional science with DES, emphasizing areas where the survey makes a difference with respect to other current surveys. The paper illustrates, using early data (from ‘Science Verification’, and from the first, second and third seasons of observations), what DES can tell us about the Solar system, the Milky Way, galaxy evolution, quasars and other topics. In addition, we show that if the cosmological model is assumed to be +cold dark matter, then important astrophysics can be deduced from the primary DES probes. Highlights from DES early data include the discovery of 34 trans-Neptunian objects, 17 dwarf satellites of the Milky Way, one published z > 6 quasar (and more confirmed) and two published superluminous supernovae (and more confirmed)