Data-Driven Sparse Structure Selection for Deep Neural Networks

Deep convolutional neural networks have liberated its extraordinary power on various tasks. However, it is still very challenging to deploy state-of-the-art models into real-world applications due to their high computational complexity. How can we design a compact and effective network without massive experiments and expert knowledge? In this paper, we propose a simple and effective framework to learn and prune deep models in an end-to-end manner. In our framework, a new type of parameter -- scaling factor is first introduced to scale the outputs of specific structures, such as neurons, groups or residual blocks. Then we add sparsity regularizations on these factors, and solve this optimization problem by a modified stochastic Accelerated Proximal Gradient (APG) method. By forcing some of the factors to zero, we can safely remove the corresponding structures, thus prune the unimportant parts of a CNN. Comparing with other structure selection methods that may need thousands of trials or iterative fine-tuning, our method is trained fully end-to-end in one training pass without bells and whistles. We evaluate our method, Sparse Structure Selection with several state-of-the-art CNNs, and demonstrate very promising results with adaptive depth and width selection.Comment: ECCV Camera ready versio

Inch by inch: Making our Gardens Grow

Why bring gardens to early education programs? School garden success has a long history, but this success has been found primarily in elementary schools (Blair 2009). Yet child care centers are typically open year-round and therefore are better prepared to enjoy the full bounty that gardens may provide. Gardening with young children isn’t new (McFarland 2005), but a small national “farm to child care movement” that supports garden programs is growing (Berkenkamp and Mader 2012). Unfortunately, many teachers are not familiar with gardening and plant science (Blair 2009). The novelty of gardening may be one of the obstacles in bringing gardens to the child care environment. Other obstacles can include concerns about working with children in messy outdoor settings and engaging in strenuous labor. So why do it? What are the benefits for young children

The Fermi Gamma-Ray Haze from Dark Matter Annihilations and Anisotropic Diffusion

Recent full-sky maps of the Galaxy from the Fermi Gamma-Ray Space Telescope have revealed a diffuse component of emission towards the Galactic center and extending up to roughly +/-50 degrees in latitude. This Fermi "haze" is the inverse Compton emission generated by the same electrons which generate the microwave synchrotron haze at WMAP wavelengths. The gamma-ray haze has two distinct characteristics: the spectrum is significantly harder than emission elsewhere in the Galaxy and the morphology is elongated in latitude with respect to longitude with an axis ratio ~2. If these electrons are generated through annihilations of dark matter particles in the Galactic halo, this morphology is difficult to realize with a standard spherical halo and isotropic cosmic-ray diffusion. However, we show that anisotropic diffusion along ordered magnetic field lines towards the center of the Galaxy coupled with a prolate dark matter halo can easily yield the required morphology without making unrealistic assumptions about diffusion parameters. Furthermore, a Sommerfeld enhancement to the self annihilation cross-section of ~30 yields a good fit to the morphology, amplitude, and spectrum of both the gamma-ray and microwave haze. The model is also consistent with local cosmic-ray measurements as well as CMB constraints.Comment: 14 pages, 9 figures; submitted to Ap

A high resolution line survey of IRC+10216 with Herschel. First results: Detection of warm silicon dicarbide SiC2

We present the first results of a high-spectral-resolution survey of the carbon-rich evolved star IRC+10216 that was carried out with the HIFI spectrometer onboard Herschel. This survey covers all HIFI bands, with a spectral range from 488 to 1901GHz. In this letter we focus on the band-1b spectrum, in a spectral range 554.5-636.5GHz, where we identified 130 spectral features with intensities above 0.03 K and a signal-to-noise ratio >5. Detected lines arise from HCN, SiO, SiS, CS, CO, metal-bearing species and, surprisingly, silicon dicarbide (SiC2). We identified 55 SiC2 transitions involving energy levels between 300 and 900 K. By analysing these rotational lines, we conclude that SiC2 is produced in the inner dust formation zone, with an abundance of ~2x10^-7 relative to molecular hydrogen. These SiC2 lines have been observed for the first time in space and have been used to derive an SiC2 rotational temperature of ~204 K and a source-averaged column density of ~6.4x10^15 cm^-2. Furthermore, the high quality of the HIFI data set was used to improve the spectroscopic rotational constants of SiC2.Comment: A&A HIFI Special Issue, 201

Cost implications of new treatments for advanced colorectal cancer: Cost-effectiveness of CRC Treatment

Since 1996, six new drugs have been introduced for the treatment of metastatic colorectal cancer. While promising, these drugs are frequently given in the palliative, and are much more expensive than older treatments. The objective of this study is to measure the cost implications of treatment with sequential regimens that include chemotherapy and/or monoclonal antibodies

Evidence for Quasi-isotropic Magnetic Fields from Hinode Quiet Sun Observations

Some recent investigations of spectropolarimetric observations of the Zeeman effect in the Fe I lines at 630 nm carried out with the Hinode solar space telescope have concluded that the strength of the magnetic field vector in the internetwork regions of the quiet Sun is in the hG regime and that its inclination is predominantly horizontal. We critically reconsider the analysis of such observations and carry out a complete Bayesian analysis with the aim of extracting as much information as possible from them, including error bars. We apply the recently developed BayesME code that carries out a complete Bayesian inference for Milne-Eddington atmospheres. The sampling of the posterior distribution function is obtained with a Markov Chain Monte Carlo scheme and the marginal distributions are analyzed in detail. The Kullback-Leibler divergence is used to study the extent to which the observations introduce new information in the inference process resulting in sufficiently constrained parameters. Our analysis clearly shows that only upper limits to the magnetic field strength can be inferred with fields in the kG regime completely discarded. Furthermore, the noise level present in the analyzed Hinode observations induces a substantial loss of information for constraining the azimuth of the magnetic field. Concerning the inclination of the field, we demonstrate that some information is available to constrain it for those pixels with the largest polarimetric signal. The results also point out that the field in pixels with small polarimetric signals can be nicely reproduced in terms of a quasi-isotropic distribution.Comment: 13 pages, 10 figures, accepted for publication in Ap

nIFTy galaxy cluster simulations – II. Radiative models

We have simulated the formation of a massive galaxy cluster (M$_{200}^{\rm crit}$ = 1.1$\times$10$^{15}h^{-1}M_{\odot}$) in a $\Lambda$CDM universe using 10 different codes (RAMSES, 2 incarnations of AREPO and 7 of GADGET), modeling hydrodynamics with full radiative subgrid physics. These codes include Smoothed-Particle Hydrodynamics (SPH), spanning traditional and advanced SPH schemes, adaptive mesh and moving mesh codes. Our goal is to study the consistency between simulated clusters modeled with different radiative physical implementations - such as cooling, star formation and AGN feedback. We compare images of the cluster at $z=0$, global properties such as mass, and radial profiles of various dynamical and thermodynamical quantities. We find that, with respect to non-radiative simulations, dark matter is more centrally concentrated, the extent not simply depending on the presence/absence of AGN feedback. The scatter in global quantities is substantially higher than for non-radiative runs. Intriguingly, adding radiative physics seems to have washed away the marked code-based differences present in the entropy profile seen for non-radiative simulations in Sembolini et al. (2015): radiative physics + classic SPH can produce entropy cores. Furthermore, the inclusion/absence of AGN feedback is not the dividing line -as in the case of describing the stellar content- for whether a code produces an unrealistic temperature inversion and a falling central entropy profile. However, AGN feedback does strongly affect the overall stellar distribution, limiting the effect of overcooling and reducing sensibly the stellar fraction.Comment: 20 pages, 13 figures, submitted to MNRA