Proposing critical realism and second-generation environmental justice for advancing sacrifice zone

23-06070-2 https://doi.org/10.18820/2415-0495/trp72i1.7 https://doi.org/10.18820/2415-0495/trp72i1.7 https://doi.org/10.18820/2415-0495/trp72i1.7 http://dx.doi.org/10.1177/27539687241236803 http://dx.doi [...] 66f49ad87248672c676562535f00521b5efa768400200050004400460020658768633002> /CHT <FEFF4f7f752890194e9b8a2d7f6e5efa7acb7684002000410064006f006200650020005000440046002065874ef653ef5728684c9762537088686a5f [...] /CHS <FEFF4f7f75288fd94e9b8bbe5b9a521b5efa7684002000500044004600206587686353ef901a8fc7684c976262535370673a548c002000700072006f006f00660065007200208fdb884c9ad88d2891cf62535370300260a853ef4ee54f7f752800200 …

5-X6)*(Y5-Y8)))+ * (DABS((X7-X6)*(Y7-Y8)*(Z7-Z3)+(Y7-Y6)* * (Z7-Z8)*(X7-X3)+(Z7-Z6)*(X7-X8)*(Y7-Y3)- * (X7-X3)*(Y7-Y8)*(Z7-Z6)-(Y7-Y3)*(Z7-Z8)* * (X7-X6)-(Z7-Z3)*(X7-X8)*(Y7-Y6)))+ * (DABS((X1-X3)*(Y1 [...] Y1+A7*DY2+A7*DY3+A5*DY4+A5*DY5+A6*DY6 C DX2(KAREA2(3,IELH5))=A7*DY1+A5*DY2+A6*DY3+A7*DY4+A6*DY5+A5*DY6 DX2(KAREA2(3,IELH6))=A7*DY1+A6*DY2+A5*DY3+A6*DY4+A7*DY5+A5*DY6 DX2(KAREA2(3,IELH7))=A7*DY1+A7*DY2 [...] A6*DY3+A7*DY4+A6*DY5+A7*DY6 DX2(KAREA2(3,IELH2))=A5*DY1+A6*DY2+A5*DY3+A6*DY4+A7*DY5+A7*DY6 DX2(KAREA2(3,IELH3))=A5*DY1+A7*DY2+A6*DY3+A5*DY4+A6*DY5+A7*DY6 DX2(KAREA2(3,IELH4))=A5*DY1+A6*DY2+A7*DY3+A6*D …

Learning Important Features Through Propagating Activation Differences

showed that this was similar to deconvolutional networks (Zeiler & Fergus, http://goo.gl/qKb7pL http://goo.gl/qKb7pL http://goo.gl/RM8jvH DeepLIFT: Learning Important Features Through Propagating Activation [...] data, and show significant advantages over gradient-based methods. Video tutorial: http://goo.gl/ qKb7pL, code: http://goo.gl/RM8jvH. 1. Introduction As neural networks become increasingly popular, their [...] reduce noise relative to using RevealCancel on all layers. (b) proportion of strong matches (log-odds> 7) to TAL1 motif in regions containing both TAL1 and GATA1 that had total score≤ 0 for task 0; Guided …

report13-title.dvi

21 10 coverage 91% 82% 60% 87% sg line sg jump sg convex sg concave #rules 43 36 2 3 coverage 87.7% 89.7% 84.9% 89.0% through door (parallel) through door (diagonal) #rules 2 1 coverage 100% 100% Table [...] thresholds when fed with features computed on the base of image's pixelwise gradients (see also gure 4). 7 The second task to be solved is to provide the robot with means to correct its estimation of its position [...] specializes it until the given positive and negative examples are suciently well described. 1 2 3 4 5 6 7 8 9 0 10 11 0 1 2 3 operational concepts applied to learned from sensing and action sensor features …

sequence of papers followed which outlined analysis of the method as well as numerical experiments [6, 7, 8, 9, 10]. The treatment of the deformation tensor as a distinct variable increases the problem size [...] (2ηpD (u) + E)=0 λ(2ηp ∇ D (u) + ∇ E) + exp ( λκ ηp (Tr (2ηpD (u) + E)) ) (2ηpD (u) + E) = 2ηpD (u) (7) where κ ∈ [0, 1] is the extensibility parameter. The constitutive equation of the Giesekus and PTT [...] viscoelastic formulation velocity, pressure and stress field is Q2/P disc 1 /Q3 shown in Figure 1. 7 Q2 P disc 1 Q3 Figure 1: Finite elements Q2,P disc 1 ,Q3 for velocity, pressure and stess, respectively …

Microsoft Word - Ghalami_19102011_proof.doc

pair of the animals was kept in a cage at 22±2 ˚C with a constant light-dark cycle (light from 7:00 AM to 7:00 PM). Sufficient water and food was always provided for the animals except during the experiment [...] group. Normal diet Day 1 4 7 11 15 18 21 24 27 30 Food intake (g/day) 19.07 ±1.01 17.47 ±0.21 20.2 ±1.59 18.07 ±0.55 16.5 ±0.37 20.37 ±1.47 17.87 ±0.37 17.67 ±0.56 19.1 ±0.61 20.7 ±1.37 Water intake (ml/day) [...] 39 ±4.11 136.67 ±3.37* 151.71 ±4.8 145.08 ±3.61 170.22 ±6.53§ 161.02 ±7.65 158.39 ±4.25§ 139.93 ±2.41***Ф 173.14 ±6.86§§ 164.4 ±7.12 Free fatty acid (µmol/l) 653.17 ±63.86 584 ±80.46 514.83 ±24.22 467 …

167532.pdf

reason (as good as any other) to call these particular vectors “support vectors”10. 3.7. Examples by Pictures Figure 7 shows two examples of a two-class pattern recognition problem, one separable and one [...] 157 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 100 200 300 400 500 600 700 800 900 1000 Ac tu al R isk : SV B ou nd : VC B ou nd Sigma Squared 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0 100 200 300 400 500 600 700 800 [...] {$1, 1}. (6) Then f(!) gives this labeling if I choose ! to be ! = $(1 + l " i=1 (1 $ yi)10i 2 ). (7) Thus the VC dimension of this machine is infinite. Interestingly, even though we can shatter an arbitrarily …

PYBACT: AN ALGORITHM FOR BACTERIAL IDENTIFICATION

Melibiose   V.  cholerae   7   99   99   78   1   V.  mimicus   21   99   99   99   0   V.  metschnikovii   50   100   96   100   0   V.  cincinnatiensis   0   100   100   100   7   V.  holisae   0   0   [...] 5   0   95   0   M.  morganii  biogroup  1   100   0   100   0   M.  morganii  subsp.  sibonii   7   7   100   0   O.  proteus  biogroup  2   0   0   15   0   P.  dispersa   27   0   9   100   P.  luminescens [...] V.  holisae   0   0   100   0   0   V.  damsela   0   0   90   0   0   V.  fluvialis   0   0   96   7   0   V.  furnissii   0   0   100   55   0   V.  alginolyticus   0   0   20   80   0   V.  parahaemolyticus …