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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

Katy Auckland
2011 British Journal of Psychiatry  
doi:10.1192/bjp.bp.110.084459 fatcat:gru4fudouzgbvjs6jz2tqrz5ie

Sparse Canonical Correlation Analysis [article]

David R. Hardoon, John Shawe-Taylor
2009 arXiv   pre-print
Acknowledgment 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) .  ... 
arXiv:0908.2724v1 fatcat:hhy2vv5r4jhy7hkr3cwnxjc4du

Residual Limb Pain

David R. Lindsay, Srinivas Pyati, Thomas E. Buchheit, Andrew Shaw
2012 Anesthesiology  
Lindsay, Pyati, Buchheit, and Shaw for their interest in our study 1 and for raising the important issue of residual limb pain.  ... 
doi:10.1097/aln.0b013e31823bbfcd pmid:22185879 fatcat:5acmcjhwszhjtouqarohfa7a3i

Sparse canonical correlation analysis

David R. Hardoon, John Shawe-Taylor
2010 Machine Learning  
Acknowledgements 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  ... 
doi:10.1007/s10994-010-5222-7 fatcat:7auc22m3mbfcrop5yxpt4wsn6q

Nonlinear dynamic interpretation of quantum spin [article]

Joshua J. Heiner, Harry C. Shaw, David R. Thayer, Joshua D. Bodyfelt
2018 arXiv   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
more » ... ry to reproduce the correct statistical quantum results.
arXiv:1811.02624v1 fatcat:htnx3odh7vgn7jra52dtdulfqm

A Nonconformity Approach to Model Selection for SVMs [article]

David R. Hardoon, Zakria Hussain, John Shawe-Taylor
2009 arXiv   pre-print
. , n. where b is the bias term, ξ ∈ 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} .  ... 
arXiv:0909.2332v1 fatcat:kuawuqlsqjcjhli2canhhfcfei

Morphologic Variation in Lumbar Spinal Canal Dimensions by Gender, Race and Age

Jeremy D. Shaw, Jason Eubanks, Daniel R. Cooperman, Ling Li, Daniel L. Shaw, David H. Kim
2012 The spine journal  
doi:10.1016/j.spinee.2012.08.308 fatcat:2lzg67rkezghvb5ny2zbi3ogqi

Infoomation Technology for Enterprise Integration

Michael J. Shaw, Andrew B. Whinston, Benn R. Konsynski, Robert W. Blanning, David R. King
1994 International Conference on Information Systems  
integration; Benn Konsynski will look at information technology for inter-organizational coordination; Robert Blanning will address the information technology infrastructure for enterprise integration; and David  ... 
dblp:conf/icis/ShawWKBK94 fatcat:bimajh6t3feupozz2vbckcuxce

Evaluating Various Water Stress Calculations in RZWQM and RZ-SHAW for Corn and Soybean Production

Joseph A. Kozak, Liwang Ma, Lajpat R. Ahuja, Gerald Flerchinger, David C. Nielsen
2006 Agronomy Journal  
approach (ET 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,  ... 
doi:10.2134/agronj2005.0303 fatcat:e7b64d5w25bitf2qcsprdn7xrm

PAC-Bayes Analysis Of Maximum Entropy Classification

John Shawe-Taylor, David R. Hardoon
2009 Journal of machine learning research  
This new representation of the data in the columns of matrix 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 = RR 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.  ... 
dblp:journals/jmlr/Shawe-TaylorH09 fatcat:looymk4z6fcd3kdss6hkstjkoi

Howard Roderick Duval John Daniel Griffiths David Eryl Meredith Hugh Stewart Kerr Sainsbury Norman Tate

R. E Shaw
2001 BMJ (Clinical Research Edition)  
doi:10.1136/bmj.322.7300.1493 fatcat:oevuulk66zgflcp5bmr2ockyam

Two view learning: SVM-2K, Theory and Practice

Jason D. R. Farquhar, David R. Hardoon, Hongying Meng, John Shawe-Taylor, Sándor Szedmák
2005 Neural Information Processing Systems  
Theorem 2. [2] If κ : X × X → 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  ... 
dblp:conf/nips/FarquharHMSS05 fatcat:gjtlmlkk4bb3xb67o5czb6uuvy

Inferring LISP Programs From Examples

David E. Shaw, William R. Swartout, C. Cordell Green
1975 International Joint Conference on Artificial Intelligence  
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  ... 
dblp:conf/ijcai/ShawWG75 fatcat:dp3o2goysvfbxkthgczluyccce

Matching Pursuit Kernel Fisher Discriminant Analysis

Tom Diethe, Zakria Hussain, David R. Hardoon, John Shawe-Taylor
2009 Journal of machine learning research  
of K[i, i] −1 such that 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  ... 
dblp:journals/jmlr/DietheHHS09 fatcat:w2mtdoh5krazlpeoqdi3kxvip4

Deriving molecular bonding from macromolecular self-assembly [article]

Fabien Silly, Ulrich K. Weber, Adam Q. Shaw, Victor M. Burlakov, Martin R. Castell, G. A. D. Briggs, David G. Pettifor
2008 arXiv   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
more » ... 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.
arXiv:0803.0213v1 fatcat:xl7mnhv4xffpznp7a4ptn5s244
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