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Textbook of Pediatric Psychosomatic Medicine. Edited by Richard J. Shaw & David R. DeMaso. American Psychiatric Publishing. 2010. US$135.00 (hb). 551pp. ISBN: 9781585623501

2011
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British Journal of Psychiatry
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Sparse Canonical Correlation Analysis
[article]

2009
*
arXiv
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pre-print

Acknowledgment

arXiv:0908.2724v1
fatcat:hhy2vv5r4jhy7hkr3cwnxjc4du
*David**R*. Hardoon is supported by the EPSRC project Le Strum, EP-D063612-1. We would like to thank Zakria Hussain and Nic Schraudolph for insightful discussions. ... The extracted projection directions can be computed Algorithm 4 The SCCA algorithm with deflation input: Data matrix X ∈*R*m×ℓ , Kernel matrix K ∈*R*ℓ×ℓ . ... ; Hardoon &*Shawe*-Taylor, In Press) . ...##
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Residual Limb Pain

2012
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Anesthesiology
*

Lindsay, Pyati, Buchheit, and

doi:10.1097/aln.0b013e31823bbfcd
pmid:22185879
fatcat:5acmcjhwszhjtouqarohfa7a3i
*Shaw*for their interest in our study 1 and for raising the important issue of residual limb pain. ...##
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Sparse canonical correlation analysis

2010
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Machine Learning
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Acknowledgements

doi:10.1007/s10994-010-5222-7
fatcat:7auc22m3mbfcrop5yxpt4wsn6q
*David**R*. Hardoon is supported by the EPSRC project Le Strum, EP-D063612-1. ... Algorithm 1 The SCCA algorithm input: Data matrix X ∈*R*N×ℓ , Kernel matrix K ∈*R*ℓ×ℓ and e k = 1. % Initialisation: w = 0, j = 1 µ = 1 M P M i |(2XKe) i | γ = 1 N P N i |(2K 2 e) i | α − = 2X ′ Ke + µj ...##
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Nonlinear dynamic interpretation of quantum spin
[article]

2018
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arXiv
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pre-print

In an effort to provide an alternative method to represent a quantum spin, a precise nonlinear dynamics semi-classical model is used to show that standard quantum spin analysis can be obtained. The model includes a multi-body, anti-ferromagnetic ordering, highly coupled quantum spin and a semi-classical interpretation of the torque on a spin magnetic moment in the presence of a magnetic field. The deterministic nonlinear differential coupling equation is used to introduce chaos, which is

arXiv:1811.02624v1
fatcat:htnx3odh7vgn7jra52dtdulfqm
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... ry to reproduce the correct statistical quantum results.##
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A Nonconformity Approach to Model Selection for SVMs
[article]

2009
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arXiv
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pre-print

. , n. where b is the bias term, ξ ∈

arXiv:0909.2332v1
fatcat:kuawuqlsqjcjhli2canhhfcfei
*R*n is the vector of slack variables and w ∈*R*n is the primal weight vector, whose 2-norm minimisation corresponds to the maximisation of the margin between the set ... This corresponds to bounding the difference between true and empirical probabilities over the sets A = {(−∞, a] : a ∈*R*} . ...##
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Morphologic Variation in Lumbar Spinal Canal Dimensions by Gender, Race and Age

2012
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The spine journal
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Infoomation Technology for Enterprise Integration

1994
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International Conference on Information Systems
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integration; Benn Konsynski will look at information technology for inter-organizational coordination; Robert Blanning will address the information technology infrastructure for enterprise integration; and

dblp:conf/icis/ShawWKBK94
fatcat:bimajh6t3feupozz2vbckcuxce
*David*...##
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Evaluating Various Water Stress Calculations in RZWQM and RZ-SHAW for Corn and Soybean Production

2006
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Agronomy Journal
*

approach (ET

doi:10.2134/agronj2005.0303
fatcat:e7b64d5w25bitf2qcsprdn7xrm
*SHAW*) in RZ-*SHAW*. ... However, RZ-*SHAW*with ET*SHAW*provided less accurate simulations for corn and soybean growth. ... For ET*SHAW*, the total AT rate for a single crop species j (T j ) is calculated as follows, T j 5 O NC i¼1*r*vs,i,j 2*r*v,i*r*s,i,j 1*r*h,i,j LAI i,j [8] where NC is the number of canopy layers,*r*vs, ...##
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PAC-Bayes Analysis Of Maximum Entropy Classification

2009
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Journal of machine learning research
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This new representation of the data in the columns of matrix

dblp:journals/jmlr/Shawe-TaylorH09
fatcat:looymk4z6fcd3kdss6hkstjkoi
*R*,*r*i , which gives the exact same kernel matrix. φ : φ(x i ) →*r*i . where*r*i is the ith column of*R*. ... K = X ′ X =*R*′ Q ′ QR =*R*′*R*The computation of*R*ij corresponds to evaluating the inner product between φ(x i ) with the new basis vector q j for j < i. ...##
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Howard Roderick Duval John Daniel Griffiths David Eryl Meredith Hugh Stewart Kerr Sainsbury Norman Tate

2001
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BMJ (Clinical Research Edition)
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Two view learning: SVM-2K, Theory and Practice

2005
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Neural Information Processing Systems
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Theorem 2. [2] If κ : X × X →

dblp:conf/nips/FarquharHMSS05
fatcat:gjtlmlkk4bb3xb67o5czb6uuvy
*R*is a kernel, and S = {x 1 , • • • , x ℓ } is a sample of point from X, then the empirical Rademacher complexity of the class F B satisfies Rℓ (F) ≤ 2B ℓ ℓ i=1 κ (x i , ... Then with probability at least 1 − δ over random draws of samples of size ℓ, every f ∈ F satisfies E D [f (x)] ≤ E S [f (x)] +*R*ℓ (F) + 3 ln(2/δ) 2ℓ ≤ E S [f (x)] + Rℓ (F) + 3 ln(2/δ) 2ℓ a training set ...##
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Inferring LISP Programs From Examples

1975
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International Joint Conference on Artificial Intelligence
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*Shaw*and was revised by William Swat tout. ... evaluation in fact yields the user-specified output, the function is presented to the user for verification and further user testing SECTION b -CONCLUSION The EXAMPLE program was written in INTERLISP by

*David*...

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Matching Pursuit Kernel Fisher Discriminant Analysis

2009
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Journal of machine learning research
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of K[i, i] −1 such that

dblp:journals/jmlr/DietheHHS09
fatcat:w2mtdoh5krazlpeoqdi3kxvip4
*R**R*= K[i, i] −1 . ... We begin by applying the Nyström method of lowrank approximation of the Gram matrix [Williams and Seeger, 2001 ] K = K[:, i]K[i, i] −1 K[:, i] = K[:, i]*R*RK[:, i] , where*R*is the Cholesky decomposition ...##
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Deriving molecular bonding from macromolecular self-assembly
[article]

2008
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arXiv
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pre-print

Macromolecules can form regular structures on inert surfaces. We have developed a combined empirical and modeling approach to derive the bonding. From experimental scanning tunneling microscopy (STM) images of structures formed on Au(111) by melamine, by PTCDA, and by a 2:3 mixture of the two, we determine the molecular bonding morphologies. Within these bonding morphologies and recognizing the distinction between cohesive and adhesive molecular interactions we simultaneously simulated

arXiv:0803.0213v1
fatcat:xl7mnhv4xffpznp7a4ptn5s244
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... molecular structures using a lattice Monte Carlo method. Within these bonding morphologies there is a distinction between cohesive and adhesive molecular interactions. We have simulated different molecular structures using a lattice Monte Carlo method.
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