Contributions in Inspire:
C95-04-03
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Contributions to this conference in CDS
| Experience using formal methods in high energy physics, pt.2 (p. 3) |
| by Balke, A C |
| Integration of symbolic computing in accelerator control (p. 9) |
| by Arruat, M |
| Software engineering methods and standards used in the Sloan digital sky survey (p. 23) |
| by Petravick, D |
| Quality assurance on coupling DAQ software modules (p. 29) |
| by Maidantchik, C |
| Renormalization group symmetry and Sophus Lie group analysis (p. 111) |
| by Shirkov, D V |
| Symbolic manipulation in theoretical high energy physics (p. 179) |
| by Nason, P |
| Results from a neural trigger based on the MA16 microprocessor (p. 359) |
| by Baldanza, C |
| Tagging the s quark in the hadronic decays of the Z (p. 377) |
| by Cossutti, F |
| Particle searches with neural nets (p. 383) |
| by Stimpfl-Abele, G |
| Application of artificial neural networks to low $p_T$ muon identification in ATLAS hadron calorimeter (p. 515) |
| by Budagov, Yu A |
| Neural second-level trigger system based on calorimetry (p. 545) |
| by Seixas, J M |
| Determination of beam parameters for LEAR with neural nets (p. 595) |
| by Stimpfl-Abele, G |
| On a possible second-level trigger for the experiment DISTO (p. 617) |
| by Bussa, M P |
| New random number generator on the base of 2D-cellular automaton (p. 635) |
| by Ososkov, G A |
| Neural networks applied to nuclear physics (p. 709) |
| by David, C |
| Selection of variables for neural network analysis : comparisons of several methods with high energy physics data(p. 719) |
| by Proriol, J |
| Controlled neural network application in track-match problem (p. 731) |
| by Baginyan, S A |
| Numerical approach to two-loop integrals |
| by Fujimoto, J |
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Record created 1996-04-26, last modified 2012-01-30