Heterogeneous High-Performance Computing

A Bibliography of Publications in The InternationalJournal of Supercomputer Applications,The International Journal of Supercomputer Applications and High-Performance Computing,and TheInternational Journal of High PerformanceComputing ApplicationsNelson H.F.BeebeUniversity of UtahDepartment of Mathematics,110LCB155S1400E RM233Salt Lake City,UT84112-0090USATel:+18015815254FAX:+18015814148E-mail:beebe@,beebe@,beebe@(Internet)WWW URL:/~beebe/12April2006Version1.32Title word cross-reference 3[GGS01].d=2[BRT+92].CH+H2 CH∗3 CH2+H[ASW91]. CuO2[SSSW91].K2[CBW95].N[SWW94]. -Body[SWW94].-D[GGS01]./I[CHZ02].0th[RAGW93].100[IHM87].10P[DD89].1917-1991[Mar91].2[DD89].200/VF[DD89].3[THL88].3-D[THL88].3.0[BRM03]. 3090[DD89].3090-200[DD89].3090-200/ VF[DD89].31G*[PUR94].3800[WOG95].125[HRM89].5/SE[KJH96].6[PUR94].6-31G*[PUR94].90[DL97].A&M[Nas92].Access[WHL03]. Accessing[HLP+03].Accurate[TMWS91].Acoustic[GKN+96]. Active[Her91].Ad[BG02].Ada[Kok88]. Adapting[DE03].Adaptive[AH93]. Additive[PR95].Administration[SDA+01].Adsorption[CH94].Advances[KKDV03]. Aerodynamics[YM91].Agents[QWIC02]. agricultural[SH93].Aided[MM90].AIX[Ano01a].Alamos[BBB+91b]. Algebra[GJM88].Algorithm[GJM88]. Algorithms[KL87].All-to-All[BJ92]. Alliant[DD91].Allocation[WPBB01]. alpha[TKSK88].Amdahl[HE01].amines[PUR94].ammonium[PUR94]. Amplitude[BGK+90].analogs[PUR94]. Analysis[MB87,LS90].Analytic[MA89]. Analyze[KKCB98].Analyzers[Ano01a]. Analyzing[WPBB01].Anatomy[YFH+96].Animal[UB95]. animated[LSS93].Animation[SS89]. Aperture[MPG93].API[BH00]. Appendix[Ano01a].Appendixes[Ano01a].AppLeS[SBWS99]. Application[NKR90].Applications[Ano98a].Applied[vL+03]. Applying[Dem90].Approach[FBW87]. Approximate[Cho01].Aqueous[PRT90]. Architectural[Gro03].Architecture[Ish91].architectures[JO92].Area[DFP+96]. ARION[HLP+03].Arising[Ma00]. Arithmetic[BSB89].Army[Aus92].array[JO92].Arrival[Wit92].Aspects[ZOF90].Assessing[ACM88]. Assessment[ZOF90].Assist[BB02]. asynchronous[PH91].Atmosphere[HAF+96].Atmosphere-Ocean[HAF+96]. Atmospheric[ARR99].Atomic[IHM87]. Attributes[Del93].Automata[RE87]. Automatic[Cza03].Automobile[HTSK90].Autonomous[SKB01].Availability[Pra01].Aware[YBA+03]. Axisymmetric[SG91].B[Ano01a].Band[Tho90].Based[Nak99]. Basic[JO92,Don02a].Bay[WLVL+96]. Beamforming[CYT+02].Bearing[FFNP97].Behavior[AK93]. Benchmarking[BRT+92].Benchmarks[BCK89,BBB+91a].Benefits[ACM88].Beowulf[SS99].Best[Lee03].Beyond[SBF90].Binary[DIB00].Biofluid[RKKC90]. Biological[WW92].Biology[SSNM92]. Biomembranes[SABK94].BLAS[DD89]. Blast[Don02a].Block[BS88].Body[TMWS91].Bone[HOPB92]. Boundary[SG91].Bridging[SS99]. Broadcast[BJ92].Builder[DL97]. Building[Wit92].Bulk[DGP+97].Butterfly[Kum89].C[Poz97].C90[ABF+99].Cache[MBW87].Cache-Coherent[Wad99].Cactus[AAF+01].Calculation[ACG+90]. Calculational[ZOF90].calculations[TKSK88].Caltech[Din91]. Caltech/JPL[Din91].Campus[GNTLH97].Campus-Wide[GNTLH97].Can[Pan97]. Cancers[GKB93].Capacity[BL99]. Carcinogens[HB90].Cards[Gro03]. Carlo[MB87].Carolina[LC90].Case[WGI90].CBVE[WLVL+96]. CEBAF[DZDR95].Center[All88,BBW90].Centers[All88]. CFD[GKMT00].CGNR[Man97].3Challenge[Kit90].Changing[MMS88]. Characterization[LPJ98].Chemical[TW87].Chemically[MYC92]. ChemIO[NFK98].Chemistry[EDS95]. Chesapeake[WL VL+96]. Chromodynamics[Liu90].Circular[AEPR92].Circulation[KM95]. CLAS[DZDR95].Classification[Tho90]. Climate[WHL03].Climatic[WBMY90]. Clouds[Tho90].Club[BCK89].Cluster[KT99].Clustering[NRR97]. Clusters[DT99].CM[CC95].CM-2[CC95].CM-5[KJH96].CM-5/SE[KJH96].CM2[CH94].Co[Mat03].Co-reservation[Mat03].Co-scheduling[Mat03].Coarse[BGB+96]. Coarse-Grained[BGB+96].Code[MSK92].Codes[IHM87].Coherent[Wad99].Collaborative[NBB+96].Collaboratory[YFH+96].Collapse[HTSK90].Collections[HLP+03]. Collide[NBB+96].Color[Tho90].Color/ Albedo[Tho90].Combining[Gir02]. Communication[BBDR95]. Communication/Computation[BBDR95].Community[HBSM03].Comparative[MOK00].Comparing[BF01].Comparison[Gen88]. Comparisons[Ma00].Compilers[Ano01a]. Complete[LK01].Complex[GKB93]. Complexity[BGB+96].Component[KBA00].Compositional[KR94,KR95]. Compounds[FWZ91].Compression[DLY+98].Computation[Her88,TR92]. Computational[FBW87].Computations[Duk91].Computer[TW87].Computer-Aided[MM90].Computers[Meu88].Computing[Ewi88,Lee03].Concurrent[MBW87].Conference[KKDV03].Configuration[AEPR92].Confined[ACG+90].Conjugate[Mel87]. Connection[HZ91].Conquer[Cza03]. Constant[MP94].Constrained[NKR90]. Constraints[GSHL03].Contaminant[ABF+99].Context[YBA+03].Context-Aware[YBA+03].Contributors[Ano96b].Control[AK91]. Controlled[DSD+91].convex[SH93]. Coordinate[YRA+02].Coordinated[FP02].CORBA[P´e r03]. Correspondence[IS96].Cortical[WW92]. Coscheduling[BL99].Coupled[HAF+96]. Coupling[P´e r03].CPU[BL99].Crash[HTSK90,CEL+97].CRAY[THL88].CRAY-2[DD89].CRAY-T3E[Ma00].Creutz[BRT+92]. CRPC[CDP+94].Crystal[Cla91]. Crystallography[CTH+93].CUMUL VS[GKP97].CYBER[ABA87]. CYDRA[HRM89].CYDRA-5[HRM89]. D[THL88].DAMPVM[Cza03]. DAMPVM/DAC[Cza03].Data[KBH88]. Data-Intensive[KUE+00].Data-Parallel[HJ96].Dataflow[ACM88]. Datasets[SE92].Davidson[UF89]. Dealing[GSHL03].Debuggers[Ano01a]. Decomposition[Meu88].Decoupled[PH91].Dedicated[GSHL03]. Delay[Rao02].demand[dPIdA03].Dense[Ede93].Department[Kit90]. Deployable[GCL93].Deployment[GCL93].Deposition[MD99]. derivatives[Haj93].Design[GJM88]. Detailed[EDS95].Detector[DZDR95]. Determination[KBH88].Determined[CGB+94].Development[HRM89].device[Lai93]. Devices[RKKC90].Diagrams[FWZ91]. Dielectric[ZOF90].Difference[THL88].4Differential[Meu88].Diffusion[TW87]. Digital[MPG93].dimensional[KS89]. Dimensionality[BFLL99].Dimensions[TW87].Dip[LT90].Direct[CM97].Direction[Mah90]. Directions[Fol90a].Discharge[YW93]. Discovery[AAF+01,AEG+03].Discrete[Ham91].Disk[KNP87]. Disordered[KVY+90].Dissemination[GL97].Dissolution[Cla91].Distance[HME90]. Distributed[MW AR87].Distributed-Memory[MCW+00]. Distributing[CBSB01].Divide[Cza03]. Divide-and-Conquer[Cza03].DNA[HB90].DOE[HBSM03].Domain[Meu88].Domain-Specific[CDH+97b].Double[PRT90].Drive[HE01].Driven[CHZ02].Dual[Ish91].Dual-Level[BBC+00].DV[TKSK88].DV-X[TKSK88].Dynamic[ABA87]. Dynamical[FBW87].Dynamics[Gen88]. e-Science[HWP03].Early[HGD91]. Econometric[Pet87].Economic[NKR90]. Economics[AK91].Eddy[CK01].Editor[dA03].Editorial[Don92]. Education[Mah90].Effective[BCK89].Effects[WBMY90,Haj93].Efficiency[ABA87].Efficient[Mel87]. Eigenvalue[UF89].Eigenvalues[KC92]. Electromagnetic[DGP+97].Electron[KVY+90].Electronic[FWZ91]. Electroweak[BGK+90].Element[KM95]. Eliminating[HME90].Embedded[KK01]. Embedded/Real[KK01].Embedded/ Real-Time[KK01].Enabled[CD97]. Enabling[FKT01].Encoding[DLY+98]. End[Rao02].End-To-End[Rao02]. Endangered[BB02].energies[PUR94]. Energy[IHM87,Kit90].Engineering[MMS88].Enhancement[AAC+97].Enhancements[BDG+95].Entity[BGF02].Entropy[CBW95]. Environment[CCH+88,WL VL+96]. Environmental[DLY+98].Environments[MA89].Equation[Fro91]. Equations[Syz87].Equilibration[NKR90].Equilibrium[NK89].Erratum[KR95]. estimation[SH93].ETA[DD89].ETA-10P[DD89].EuroPVMMPI[KKDV03].Evaluate[WGI90].Evaluating[BBDR95]. Evaluation[BCK89].Event[NRR97]. Events[BG00].Evolution[WJS+90]. Exact[ZK93].Example[NBB+96]. Excited[WLC91].Excited-State[WLC91].Excitement[RAGW93].Expand[GCCC+03].Expect[Pan92]. Experience[HGD91].Experiences[Reu92].Experiment[HME90].Experimental[KL87].Experiments[AK91].Exploration[KPM+96].Exploring[HAF+96].Expression[RS03]. Expressions[BBDR95].Extreme[KC92].F ACOM[IHM87].Factor[DH96]. Factorization[DD89].factorizations[DEKL92].Farming[CKPD99].fast[TKSK88,KNP87].Fault[GKP97]. Faulty[LK01].Feasibility[KR94]. Feature[PTGB02].features[PUR94]. February[Sci92].Feedback[CGB+94]. Feedback-Scaling[CGB+94].Fermions[ZK93].Fernbach[Mar91]. FETI[GCD97].FFT[Wad99].FFT-Based[GGS01].field[PUR94].File[GCCC+03].Film[MD99].Financial[HZ91].Fine[ACM88].Fine-Grain[ACM88].Finite[THL88]. Finite-Element[MS02].First[DQFW90].5Flames[SG91].FLO67[WLB92].Floating[BSB89].Flow[HKK88].Flowfield[MKG90].Flows[MYC92].Fluid[Gen88].Fluid-Structure[KT99]. Fluorinated[DFC90].Fock[KKCB98]. force[PUR94].Forming[CM97].Fortran[KR94].Forum[Don02a]. Forward[THL88].Foundation[Web91]. Four[Tho90].Four-Band[Tho90].Fourier[KNP87].FPS[LT88].Fracture[BG00].Framework[vL+03]. Frankenstein[Wit92].Frontwidth[MBW87].Fueling[Her91]. Fujitsu[Ish91].Full[AEPR92].Fully[YW93].Fun[RAGW93].Function[ZOF90].Fundamental[MR90]. Fusion[ACG+90].Future[BSB89].FX[DD91].FX/80[DD91].Galaxies[Her91].Games[EGMP93].Gap[SS99].Gas[MKG90].Gases[WBMY90].Gauge[Mor89a].Gene[RS03].Generation[DE03].Genetic[RS03].GFLOP[SBF90].Glass[YSN90].Global[WBMY90]. Globalized[GKMT00].Globally[SH93]. Globus[FK97].GloVE[dPIdA03].Glow[YW93].Gluons[BRE+90]. Goodput[BL99].Gradient[Mel87]. Gradient-like[CSV91].GrADS[BCC+01]. Grain[ACM88].Grained[BGB+96]. Grand[Kit90].Graphs[LK01]. Gravitational[SWW94].Gravity[Ham91]. Greenbook’[HBSM03].Greenhouse[WBMY90].Grid[CKPD99,FKT01].Grid-based[LM03].GridLab[A+03]. Grids[DT99,vL+03].Groundwater[MMD98].Growth[Cla91]. Guest[dA03].Guided[F+03].Gyrofluid[KPM+96].Hadron[Liu90].Harbor[BBC+00]. Hartree[KKCB98].Hartree-Fock[KKCB98].Head[GKB93]. Heavy[Reu92].Heavy-Ion[Reu92]. Helium[Fro91].Helix[PRT90]. Helmholtz[BEF+95].Heterogeneous[RAGW93].Hierarchical[GJM88].High[THL88]. High-Level[BCC+01].High-Order[CC95].high-performance[Fer90].High-Pressure[WLC91].High-Wave[BEF+95].Higher[Mah90]. Highly[Sim90].history[Bra91].Hitachi[WOG95].Hoc[CHZ02,BG02]. Homotopy[DZRS99].Hoshen[CBZ97]. Hoshen-Kopelman[CBZ97].Hosted[HBSM03].HPCC[CBB+96].HPF[DL97].HPF-Builder[DL97]. HPVM[CLP+99].HPVM-Based[CLP+99].Hybrid[MS02]. Hyperbolic[FG97].Hypercube[Din91,KL87].Hypercubes[LK01].I-W AY[DFP+96].I/O[PH91].IBM[DD89].Ice[ZOF90].IceT[GS99]. IEH[LK01].II[JP93].IJSGA[Hua03]. ILU[Ma00].Image[AAC+97].Imaging[CBB+96].Immersive[THC+96]. Impact[GJM88,KBH88].Implementation[Mel87]. Implementations[RR96].Implementing[YFH+96].Implications[RE87].Implicit[GKMT00]. Improving[BL99].Incomplete[IIJ93]. Increased[WBMY90].Increasing[WW92].Index[Ano96a]. Industrial[DGP+97].Inequality[NK89]. Infer[RS03].Influence[Ede93]. Information[Ano96b].Information-Driven[CHZ02]. Information-Theoretic[FWSW02]. Infrastructure[FK97].Initial[WLVL+96]. Initio[ASW91].Institute[IHM87]. Instruction[HRM89].6Instrumentation[TM99].Integer[Gro03]. Integrate[BFLL99].Integrated[CFK+94]. Integration[QWIC02].Intel[KL87]. Intensive[Mah90].Inter[FWZ91].Inter-Semiconductor[FWZ91]. Interaction[Liu90].Interactions[TMWS91].Interactive[SS89].Interface[Ano94,SLG95].Interleaving[KNP87].International[Ano98a].Internet[Rao02]. Interpretation[Fei99].Introduction[Nag93].Inverse[Cho01]. Investigation[CK01].Investigations[Mav02].Ion[Reu92].iPSC[HGD91,KR95].iPSC/860[HGD91,KR95].Irregular[Man97]. Ising[BRT+92].Issue[Fol90b].Issues[MBW87].Iterated[RR96]. Iterative[MC90].Japan[IHM87].Jini[Hua03].Jini-based[Hua03].Jumpshot[ZLGS99]. Jupiter[Tho90].Kernel[TM99].Kinetics[ARR99]. knowledge[KT94].Kopelman[CBZ97]. Krylov[GKMT00].Kutta[RR96]. Laboratory[BBB+91b].Laminar[SG91]. Language[Sha88].languages[JO92]. Large[FBW87].Large-Scale[Ewi88]. Lattice[Mor89a].Law[HE01].LBLAS[KJH96,JO92].Learning[AH93]. Legion[GNTLH97].Length[DLY+98]. Level[DD89].Libraries[DMT01].Library[CE00,Poz97].Ligature[KBA00]. like[CSV91].Limited[TW87].Linda[SSNM92].Line[LWOB97].Linear[AGL87].Link[TLG98,Pet87]. Linux[Ano01a].Liquid[DQFW90]. Livermore[WGI90].Local[BRT+92,JO92].Local-Creutz[BRT+92].Localization[CYT+02].Localized[WCE95].Logical[SR98].Long[HRM89].Looking[AK93].Loop[IS96].Loops[WGI90].Loss[ZOF90].LU[DD89].Machine[SS89,LPG88].machines[KS89]. Magnetically[ACG+90]. Magnetohydrodynamic[ACG+90]. making[KT94].Man[Wit92]. Management[HTSK90].Many[TMWS91].Many-Body[TMWS91]. Mappings[PTGB02].Market[NK89]. Market-Based[WPBB01].Markets[IIJ93].Massively[Mon89]. Matching[ZC92].Materials[KVY+90]. Mathematical[Mon89].Matrices[KC92]. Matrix[AGL87].MCell[CBSB01].MCHF[SYF96].Means[BRT+92]. Mechanics[Her88].Mechanism[DZRS99]. Medicine[SSNM92].Meetings[Ano98c]. Member[HTSK90].Memoriam[Mar91]. memories[TKSK88].Memory[MBW87]. Merging[YBA+03].Mesh[WCE95].Mesh-Iterative[MCW+00].Meshes[Ytt97].Meso[GGS01].Meso-Scale[GGS01].Message[Ano94,SLG95].Message-Passing[Ano94,SLG95]. Metacomputing[FK97].Metals[Cla91]. Metascheduling[Mat03].method[TKSK88].Methods[Mel87]. Metric[HE01].MHD[ACG+90]. Microprocessors[WT99].Microscopic[YFH+96].Microtasked[MSK92].Microtasking[HA91].Middleware[CKPD99].Migration[KL87]. MIMD[BOD+91].Mini[Gen88].Mini-Supercomputers[Gen88]. Minimization[Rao02].Minnesota[Aus92].MiPAX[HKK88]. Missions[SKB01].MM2[PUR94].Mobile[FP02].Model[ABA87].7Modeled[WJS+90].Modeling[DD87]. Models[Pet87].Modern[BDG+00].Modified[HB90].Modulo[Gro03]. Molecular[DFC90].Monitoring[L WOB97].Monte[MB87]. Motions[DFC90].Moveout[LT90].MP[LT88].MP/416[THL88].MPI[Ano94].MPI-OpenMP[MS02]. MPI2[MPI98].MPICH[GL97].Much[RAGW93].Multiblock[Ytt97]. Multibody[BGI+99].Multicommodity[NK89].Multicomputer[Man97].Multicomputers[MOK00]. Multidimensional[HL W00]. Multidisciplinary[BGB+96].Multifrontal[MBW87].Multigrid[DMT97].Multilevel[DW97]. Multimodal[FWSW02].Multiparadigm[AS00].Multiphase[ZC92].Multiphysics[MCW+00].Multiple[Mor89b].Multiprocessing[YM91].Multiprocessor[BS88].Multiprocessors[DD91]. Multiprogramming[MA89].Multitasking[THL88].Multiunit[GCL93].NAMD[NHG+96].Nanophase[Nak99]. NAS[BBB+91a].National[BBB+91b,All88].Navier[SBF90].Navier-Stokes[SBF90]. nCube[CL95].NEC[Mor89a].Neck[GKB93].Needs[HBSM03].NERSC[HBSM03].Net[AEG+03]. Netlets[Rao02].NetSolve[CD97]. Network[NZ93].Network-Based[AM00]. Network-Enabled[CD97].Networked[FWSW02].Networks[RE87]. Neural[RE87].Newton[GKMT00]. Newton-Krylov-Schwarz[GKMT00]. Next[DE03].NMR[KBH88].NOE[CGB+94].NOE-Restrained[CGB+94].Non[GSHL03].Non-Dedicated[GSHL03]. Nonequilibrium[YW93].Nonlinear[ABA87].Nonsymmetric[MC90].normal[Haj93]. North[LC90].Northern[UB95].Novel[FWZ91].NSF[Bra91].NT[CLP+99].Nuclear[IHM87].Number[FG97].Numbers[BEF+95]. Numerical[RKKC90].Numerically[Mah90].O[PH91].Oak[HGD91].Object[NHG+96].Object-Oriented[NHG+96].Ocean[KM95,JO90].ODE[BH99].Ohio[BBW90].Oil[KR94].On-Line[L WOB97].Open[LWOB97,AEG+03].Opening[PRT90].OpenMP[BBC+00]. Operating[CW01].Optimal[FG97]. Optimization[LT88].optimizations[PUR94].Optimize[KKCB98].Optimized[MSK92]. Optimizing[Mor89a].Optorsim[B+03]. Order[THL88].Organization[FKT01]. Organized[BGF02].Oriented[NHG+96]. Our[WW92].Overlap[BBDR95]. Overlapping[PR95].Overview[DFP+96]. P4[Mat95].PACE[NKP+00].Pacific[JO90].Package[SYF96].Pair[Fro91].PAM[CEL+97].PAM-CRASH[CEL+97].papers[KKDV03].Paradigm[BGB+96]. Parallel[Syz87].Parallelism[ACM88]. Parallelization[Reu92].parameter[SH93].ParaScope[CCH+88]. Park[UB95].Parkbench[HL00]. Parmetis[LDGR03].Partial[Meu88]. Particle[DD87].Partitioning[Ytt97]. Partitions[WCE95].Passing[Ano94,SLG95].8PASSION[KKCB98].Patching[BH00]. Paths[Rao02].Patterns[GKB93].PC[Ste01].PCs[AWS01].PDEs[Ma00]. 416[THL88].600J[DEKL92].80[DD91]. 860[HGD91,KR95].Albedo[Tho90]. Computation[BBDR95].DAC[Cza03]. JPL[Din91].Logical[Chu99].Real-Time[KK01].VF[DD89]. PERFECT[BCK89].Performance[IHM87].PERMAS[AJL+97].pH[MP94].Phase[YCHH90].Photon[MWAR87]. Physical[SR98].Physical/Logical[Chu99].Physician[Wit92]. Physics[MR90].Pipeline[BFLL99]. Plasma[CDD+90].Plasmas[DD87]. Platforms[BLRR01].Play[Pan97]. POEMS[BBD00].Point[BSB89].Point-SSOR[Ma00].Poisson[GGS01]. Pollution[DFH+96].Polyacetylene[ZOF90].Polyenes[AEPR92].Polymers[DFC90]. Portable[GL97].Portals[BRM03].Power[Dem90].Powerful[Mor89b]. Practical[Cho01].Practice[BR03]. Preconditioned[Mel87].Preconditioner[BBS99].Preconditioners[Ma00].Predict[VS03]. Predicting[WLC91].Prediction[SCB+95].Predictions[RIF01].Preface[DT97]. Preprocessing[DMT97].Preprocessors[Ano01a].Pressure[WLC91].Prime[Sim90]. Principles[DQFW90].Priori[Cho01]. probabilities[Haj93].Problem[UF89]. Problems[FBW87].Procedure[CGB+94]. Process[GCL93].Processing[Mor89b]. Processor[SBF90].Processors[LT88]. Production[MSK92].program[Fer90,Web91].Programming[Syz87].Programs[ACM88].Progress[AGL87]. Project[Wit92,Pet87].Promising[Gir02].Propagation[GKN+96].Properties[ACG+90].prospectus[Bra91]. Protein[KBH88].Prototypical[WLVL+96].Providing[GKP97].Proximal[NZ93]. PVM[BDG+95].PVMGeant[DZDR95]. PVODE[BH99].Qaulity[Mat03].QCD[Din91].QoS[BSCC03].Quadtree[CL95]. Quantized[Ham91].Quantum[FBW87]. Quarks[BRE+90].Quasigeostrophic[KM95].Query[S+03]. Querying[CHZ02].Queuing[Ish91]. Radar[MPG93].Radio[CBB+96]. Randomly[CYT+02].Rare[BB02].Ray[CTH+93].Reacting[MYC92]. Reaction[Koi90].Reactions[TW87]. Ready[Sim90].Real[WLC91].Real-Time[NRR97].Realistic[BR03]. Recognition[RE87].Reconfiguration[LK01].Recovery[BB02].rectangle[Haj93]. Recurrent[Syz87].Reduced[BFLL99]. Reduced-Dimensionality[BFLL99]. Reducing[DLY+98].Reduction[NRR97]. Regional[KM95].Regression[VS03]. Relative[PUR94].Relativity[RIF01]. Remeshing[LDGR03].Remote[BB02]. Replication[B+03].Representations[WW92].Requirements[LPJ98].Research[IHM87,EM89].reservation[Mat03].Reservoir[Ewi88]. Resolution[HB90].Resource[WPBB01]. Response[ZOF90].Restrained[CGB+94]. Results[PUR94].Retrospective[Mar88]. RF[YW93].Ridge[HGD91].Rigid[Nak99].Rigid-Body-Based[Nak99].RISC[Gro03].RISC-Based[Gro03].RNA[SCB+95].Role[Sab91].Roles[MMS88].Rolling[FFNP97].9Routing[MOK00].Run[DLY+98].Runge[RR96].Runge-Kutta[RR96]. Runtime[AJL+97].S[Lai93].S-3800[WOG95].S-MP[Lai93]. SAMCEF[GCD97].SAR[AAC+97]. SARA[SBWS99].SCALA[SFP02]. Scalability[HLW00].Scalable[WLB92]. scalar[KS89].Scale[Pet87].Scaling[CGB+94].Schedule[SBWS99]. Scheduling[CKPD99].Scheme[BG00]. Schemes[BS88].Schr¨o dinger[BFLL99]. Schwarz[PR95].Science[All88]. Sciences[NKR90,DGH+93].Scientific[LS90].SE[KJH96].Sea[LPJ98]. Searches[F+03].Secondary[SCB+95]. Seeing[LPG88].Seismic[CDH+97b]. Select[KKDV03].Selective[RE87].Self[BGF02].Self-Adapting[DE03].Self-Organization[FWSW02].Self-Organized[BGF02].Semantic[FP02].semiconductor[TKSK88]. Semiconductors[Cla91].Sensor[BGF02]. Sensors[FWSW02].Sequence[Jon92]. Serial[NK89].Server[CD97].Service[Mat03].Service-based[HLP+03]. Service-oriented[Hua03].Services[AEG+03].Set[PTGB02]. Severe[WJS+90].Shape[WCDS99]. Shared[MBW87].shared-memory[DEKL92].Shelf[LPJ98]. Should[Pan92].Side[HTSK90].Sidney[Mar91].Sieves[Mon89].Signal[FP02].Simple[SBWS99]. Simulating[BRE+90].Simulation[TW87].Simulations[ABA87]. Simulator[B+03].Simultaneous[ABA87]. Single[BCJ01].Singular[Ber92].Six[WOG95].Skeletonization[DIB00]. small[PUR94].Smart[Gro03].Social[NKR90].Sodium[DQFW90]. Software[Fol90a].Soil[CWHP99].Solaris[Ano01a].Solid[DQFW90].Solution[KBH88].Solutions[Fro91]. Solve[CTH+93].Solved[CSV91].Solver[PR95].Solvers[GGS01].Solving[BS88].Some[Gir02]. Sometimes[RAGW93].Sonic[WW92]. Source[CYT+02].Sowing[GL97].Space[F+03].Spaceborne[SKB01].SPAI[BBS99].Sparce[WT99].Sparse[AGL87].Sparsity[Cho01]. Special[Nag93].Species[BB02].Specific[CDH+97b].Spectral[Tho90]. Spline[Fro91].Splitting[IS96].Spotlight[MPG93].Spread[GKB93]. SSOR[Ma00].Stability[ACG+90]. Standard[Ano94,Poz97].Standards[Pan92].State[WLC91].Static[BLRR01].Statistical[Her88]. Status[MB87].Steering[GKP97].Stefan[CSV91].Stochastic[ABA87]. Stokes[SBF90].Storm[WJS+90]. Strategies[MOK00].Strategy[MCW+00]. Structural[YCHH90].Structure[Liu90]. Structured[Ytt97].Structures[KBH88]. Studies[DQFW90].Study[WJS+90]. Studying[BOD+91].Subdomains[FG97]. Subprograms[Don02a].Subroutines[KJH96,JO92]. Supercomputer[Duk91]. Supercomputers[DD87,Gen88]. Supercomputing[MMS88,All88]. Superconductors[JP93].Supersonic[MYC92].Support[CFK+94]. SUPRENUM[MST88].Sustained[MSK92].SX[Mor89a].SX-2[Mor89a].Symmetric[Gir02]. Synchronous[DGP+97].syntax[JO92]. Synthesis[CBB+96].Synthetic[MPG93]. System[MST88,GCCC+03].Systems[AGL87].T3D[ABF+99].T3E[BBS99].Tables[vL+03].Target[BG02].Task[CFK+94].Tasking[JMP02].Taxol[CGB+94].TCGMSG[Mat95].REFERENCES10Technique[WGI90].Techniques[KM95]. Technologies[Dar99].Technology[Dar00]. Televisualization[HME90].Template[Poz97].Teraflop[HLW00].Teraflop-Scale[HL W00].Teraflops[SS99]. Testing[KDL01].Texas[Nas92].Their[RE87].Theme[Hau93].Theoretic[FWSW02].Theoretical[ASW91].Theory[Mor89a]. Thermochemical[vL+03]. Thermodynamics[GKH+91].Thin[MD99].Thin-Film[MD99]. Thinning[DIB00].Third[Lee03].Three[TW87].Three-Dimensional[LT90].Time[Sim90]. times[MP95].Tokamak[DSD+91]. Tolerance[GKP97].Tomography[CDH+97b].Too[RAGW93]. Tool[Ytt97].Toolkit[FK97].Tools[SS89]. Toolset[NKP+00].Top500[Fei99]. Topologies[MOK00].Topology[Chu99]. Total[YCHH90].Toys[SS99].Trace[NRR97].Tracking[BGF02].Traffic[BG02].Training[AM00].transfer[KT94].Transfers[VS03]. Transform[DL97].Transformations[YCHH90].Transforms[KNP87].Transition[YSN90]. Transport[MB87].Tree[SWW94].Trees[LK01].Trends[Tho90].Tridiagonal[BS88].truly[KT94].Tuning[TM99].Turbine[MKG90]. Turbulence[CDD+90].Turbulent[CB95]. Turnaround[MP95].two[KS89].two-dimensional[KS89].Two-Paths[Rao02].Type[CK01,JP93]. Type-II[JP93].U.S.[Fer90].Unconstrained[LT88]. Understanding[WW92].Units[Tho90]. University[Nas92,SSNM92]. Unstructured[WCE95].usable[KT94]. use[TKSK88].Used[DFH+96].Users[Pan97].Using[THL88].Value[SG91].Variable[BGB+96]. Variable-Complexity[BGB+96]. Variational[NK89].Vector[Mel87]. Vectorization[Reu92].Vectorized[MB87].Very[KNP87].VF[DD91].VF/600J[DEKL92].via[CSV91].Vibrational[DFC90].Video[dPIdA03].Video-on-demand[dPIdA03].Virginia[GNTLH97].Virtual[BEF+95]. Vis5D[HAF+96].Vision[Sha88,LPG88]. Visual[Koi90].Visualization[SS89,HBSM03]. Visualizing[GKB93].Vivo[CBW95]. Volume[Ano96a].Vortex[JP93].VP[IHM87].VP-100[IHM87].VP2000[Ish91].Wave[BEF+95].Wavefront[HL W00].W AY[DFP+96].Weakest[TLG98].Web[Men00].Wide[DFP+96].Wide-Area[DFP+96].Wideband[CYT+02].Windows[CLP+99]. Word[HRM89].Workload[Del93]. Workshop[Lee03,LS90].Worm[AAF+01]. 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《文献检索与利用》总复习题库（Literature search and utilization review the question bank）General review questions bank of "document retrieval and utilization"First, individual choice questions1. or less is not Boolean logicA.NOTB.ORC.ANDD.NEARThe usual order of operations of 2. Boolean logic operators is ():When A. has parentheses, the parentheses are first executed; NOT, > AND > OR without parenthesesWhen B. is bracketed, the parentheses are executed first; when parentheses are not there, NOT > OR >ANDWhen C. has parentheses, the parentheses are first executed; AND >NOT > OR without parenthesesWhen D. has parentheses, the parentheses are first executed;AND, > OR > NOT without parentheses3. words and phrases? "Can be used instead of 0 or more characters?"A. more than oneB.1C.2D.34. which of the following is the abbreviation of the library public directory retrieval system?A. CalisB. NSTLC. OCLCD. OPACWhat is the unique identifier of the 5.ISSN?A. conference documentsB. standard documentsC. thesisD. JournalWhat is the unique identifier of the 6.ISBN?A. booksB. JournalC. Technology ReportD. patent documents7. which of the following databases is a full-text database?A.CPCIB.Elsevier Science DirectC.EID. SCI8. use Adobe Reader to read the files in which formatA.PDFB. VIPC. HTML9.cajviewer is the following database full text reading software:A. Superstar Digital LibraryB. VIP Chinese science and Technology Periodicals DatabaseKI CNKI full text libraryD. Wanfang Data Resources10. browsing superstar digital library, should be installed first:A. Apabi ReaderB. Adobe ReaderC. CAJ ViewerD. SSReader11. the following databases belong to the bibliographic databaseA. SCIB. ISTPD. library OPAC12.PQDT isA. conference document databaseB. dissertation databaseC. standard document databaseD. science and technology reporting database13.AD, PB, NASA, and DOE are the four largest U.S. government reports, in which NASA refers toA. administrative reportB. Energy ReportC. military reportD. Aerospace Report14. () is a large, informative and comprehensive instrument that reflects all the knowledge, categories, or basic knowledge and basic circumstances of a human being. It is called the king of the book of tools".A. dictionaryB. EncyclopediaC. YearbookD. manual15. which of the following databases do not belong to the numeric and factual databases?A. China Information BankB. search network, statistical yearbook, databaseC. country research dataD. NPC press copy information16. in which of the following search tools can you get statistics over the years?A. dictionaryB. EncyclopediaC. YearbookD. manual17. which of the following retrieval systems provides citations for journals and references?A. WEB OF SCIEB. OCLCC. OPACD. EI18. the following Boolean logic operators are.A. andB. orC.D.near19. commonly used retrieval systems areA. directory retrieval systemB. digest retrieval systemC. full text retrieval systemD. or more20. what kind of retrieval system does the online public directory retrieval system (OPAC) belong to?A. directory retrieval systemB. digest retrieval systemC. full text retrieval system21., we can sum up the general steps of information retrieval.A.1. analysis of search topics, a clear demand for information;2. sources of information, knowledge retrieval system;3. access, selected4. retrieval methods; implementation of retrieval strategy, evaluation of retrieval results;5. adjust the search strategy to obtain the required information;6. analysis of management information, rational use of informationB.1. selects information sources, 2. develops strategies and implements retrieval, 3. evaluation information, 4. adjusts retrieval strategies, and 5. analyzes and utilizes informationC. 1. develops strategies, 2. defining questions, 3. selecting information sources, 4. implementing retrieval, 5. evaluating information, and 6. analyzing and utilizing informationD.1. define problems, 2. select information sources, 3.Develop strategies, 4. implement retrieval, and 5. evaluate information22., you need to write a report on the current status of businessintelligence systems, which should focus on the following information sources.A. web pagesB. newspaperC. magazineD. all kinds of literature database23. the standard format of reference is ().A. author Title SourceB. source of famous authorsPlease mark the 24. documents: Ma Pinzhong. Study on large astronomical telescope. China space science and technology, 1993, 13 (5) P6 - 14, ISSN1000 - 758X which belongs to the type of literature ().A. booksB. Technology ReportC. JournalD. newspaper25. in retrieval language, it is natural language.A. Heading WordsB. keywordC. unit wordsD. keywords26., what kind of retrieval methods should be taken after using the references found in the literature to expand the search scope?A. tool methodB. intersection methodC. retrospective method (snowball method)D. method27. which title of the following retrieval expressions is correct in retrieving the title of the poem entitled "poetry of the Tang and Song Dynasties"?.A. (title = Tang or title = song) and title = PoetryB. title = Tang or title = song and title = PoetryC. title = Tang and title = song or title = PoetryD. title = Tang or title = song or title = Poetry28. which of the following documents belong to the special literature?A. booksB. conference documentsC. JournalD. newspaperAre the fields represented by the 29. field codes AU, AB and PY represented?A. titles, annotations, abstract typesB. authors, abstracts, and publication yearsC. descriptors, classifications, languages30. what is the largest digital library in China?A. superstarB. scholar's homeC. founderD. Wanfang31., Xiao Li students need to find some data about the national economic life, which database can be retrieved in the following?A. Chinese VIP journalsB.EIC. National Research NetworkD.EBSCO32. foreign language full-text database, search results are sorted in several ways, if you want to sort in accordance with the relevant, you should choose: ()A.dateB.sourceC.authorD.relevance33.. This is a literature retrieved in web of science. What is the type of literature in this article?:Title:, Using, desktop, computers, to, solve, large-scale, dense,, linear, algebra, problemsAuthor (s): Marques M.; Quintana-Orti G.; Quintana-Orti E. S.; et al.Conference: Symposium on High Performance Computing (HPC) Applied to Computational Problems in Science and Engineering/9th International Conference on Computational and Mathematical Methods in Science and Engineering Location: Gijon, SPAIN Date: JUN, 2009Source:, JOURNAL, OF, SUPERCOMPUTING, Volume:, 58, Issue:, 2, Special, Issue:, SI, Pages:, 145-150, DOI:,10.1007/s11227-010-0394-2,, Published:, NOV,...A. Journal PapersB. booksC. Conference PapersD. Technical Report34.. The classification numbers of commonly used foreign language and economic books in this museum are respectively:A.H, FB.H, CC.I, HE.I, F35. using the library bibliographic retrieval system, Xiao Li's classmate retrieved a book with the classification number "H319CF". What's the significance of the classification number?A. CET Band FourB. CET Band SixC. postgraduate English testD.Tofel test36. Wang students want to find the "The Washington Post" on November 7th, an article entitled "No favorites emerge in race for horse of the year" text and listen to what can be downloaded, the museum has purchased what search resources of foreign language in the database?A.Elsevier SDB. EBSCO之报纸资源c.wsnD.翡翠二、多选题1。

Rational Design of Low-Temperature Hydrogenation Catalysts:Theoretical Predictions and Experimental Verification低温加氢催化剂的设计: 理论与实践The field of heterogeneous catalysis, specifically catalysis on bimetallic alloys, has seen many advances over the past few decades. One of the main goals of the catalysis industry is to develop new materials that have novel catalytic properties. Bimetallic catalysts, which often show electronic and chemical properties that are distinctly different from those of the parent metals, offer the opportunity to design new catalytic materials with enhanced activity, selectivity, and stability [1-2] . Currently bimetallic catalysts are widely utilized in many heterogeneous catalysis [3] and electro-catalysis [4] applications.In order to understand the origins of the novel catalytic properties, bimetallic surfaces have been the subject of many experimental and theoretical studies, as summarized in several reviews [5-7] . It is now well established that bimetallic surfaces often show novel properties that are not present on either of the parent metal surfaces [5-16] . The modification effect is especially important when the admetal coverage is in the sub monolayer to monolayer regime. However, it is difficult to know a priori how the electronic and chemical properties of a particular bimetallic surface will be modified relative to the parent metals. For this reason, the study of bimetallic surfaces in the field of catalysis has gained considerable interest. There are two critical factors that contribute to the modification of the electronic and chemical properties of a metal in a bimetallic surface. First, the formation of the hetero-atom bonds changes the electronic environment of the metal surface, giving rise to modifications of its electronic structure through the ligand effect. Second, the geometry of the bimetallic structure is typically different from that of the parent metals, e.g. the average metal-metal bond lengths change. This lattice mismatch leads to the strain effect that is known to modify the electronic structure of the metal through changes in orbital overlap [16] . While studies on model bimetallic surfaces provide fundamental insights into the novel properties, in an industrially relevant supported catalyst the active metal will be present in the form of nanoparticles. As shown in Fig.1, research efforts in our group involve three parallel approaches, with the goals being to bridge the“materials gap”and“pressure gap”between fundamental surface science studies and real world catalysis. In the current review we will utilize hydrogenation reactions as examples to demonstrate how the utilization of these three parallel approaches can lead to the rational design of bimetallic catalysts with novel low-temperature hydrogenation activities.Catalytic hydrogenations are among the most commonly practiced catalytic processes, ranging from common steps in organic synthesis, to batch processes in pharmaceutical production, to stabilization of edible oils, and to petroleum upgrading processes. Because hydrogenation reactions are typically exothermic, it is advantageous to carry out these reactions at low temperatures. In the current review we will first use the hydrogenation of cyclohexene to demonstrate the feasibility of increasing the low-temperature hydrogenation activity by reducing the binding energies of atomic hydrogen and cyclohexene, which can be achieved by designing bimetallic surfaces with specific surface structures. We will then discuss several other types of hydrogenation reactions to further illustrate the advantages of bimetallic catalysts in terms of both hydrogenation activity and selectivity.1 Structures of bimetallic surfacesIn the current review we will focus mainly on bimetallic surfaces by depositing one monolayer ofa 3d transition metal on either a Pt(111) single crystal or a polycrystalline Pt substrate. As shown in Fig.2, monolayer bimetallic surfaces can have three ideal configurations: a surface 3d-Pt-Pt(111) configuration, where the 3d monolayer grows epitaxially on the surface of the Pt substrate; an intermixed configuration, where the 3d atoms reside in the first two Pt layers to some varying degree; and the unique subsurface Pt-3d-Pt(111) configuration, where the first layer is comprised of Pt atoms and the second layer is occupied with the 3d metals.Procedures for the preparation of bimetallic surface structures under ultra-high vacuum (UHV) conditions have been described in detail previously [5] . For example, the Ni/Pt(111) bimetallic surfaces have been characterized using a wide range of experimental techniques and DFT modeling [17] . When Ni is deposited with the Pt(111) surface held at 300 K, Ni atoms stay on the top-most layer to produce the Ni-Pt-Pt(111) surface configuration. If this surface is subsequently heated to 600 K, or if the monolayer deposition of Ni occurs with the Pt(111) substrate held at 600 K, most of the Ni atoms diffuse into the subsurface region to produce the Pt-Ni-Pt(111) subsurface structure. Similar surface and subsurface structures have been obtained for several other 3d metals on the Pt(111) substrate [5,18] .The ab initio calculations in the current review were performed using the Vienna Ab initio Simulation Package (V ASP) version 4.6 [19-20] . The monolayer bimetallic systems were modeled on the closed-packed Pt(111) substrate. The PW91 functional was used within the generalized gradient approximation with an energy cutoff on the basis set of 396 eV. The bimetallic systems were modeled using a periodic 2*2 or 3*3 unit cell with four metal layers, with the slabs being separated by 6 equivalent layers of vacuum in the epitaxial direction. The top two layers were allowed to relax to the lowest energy configuration while the third and fourth layers were frozen at the bulk Pt-Pt distance. More details about the DFT modeling procedures on monolayer bimetallic surfaces can be found in a recent review [5] .2 Low-temperature hydrogenation of cyclohexeneCyclohexene is used as a probe molecule to study the hydrogenation because cyclic hydrocarbons are important reaction intermediates in many refinery and petrochemical processes, in addition to serving as building blocks for many chemicals produced in the chemical industry. Furthermore, cyclohexene has several competitive reaction pathways, including decomposition, dehydrogenation, disproportionation (self-hydrogenation), and hydrogenation. Comparative studies of these reaction pathways provide an opportunity to determine how the hydrogenation activity and selectivity are affected by the formation of bimetallic surfaces.2.1 DFT and experimental studies on single crystal surfacesOne hypothesis for promoting the low-temperature hydrogenation of alkene is that both reactants, atomic hydrogen and alkene, should bond relatively weakly on the catalyst surface to facilitate the hydrogenation steps. DFT calculations were performed to estimate the values of hydrogen binding energy (HBE) on several 3d-Pt-Pt(111) and Pt-3d-Pt(111) surfaces, as shown in Fig.3A [18] . Fig.3A reveals that HBE is related to the position of the surface d-band center with respect to the Fermi level, in agreement with the trend observed in previous studies for other surfaces [5] . In general, the addition of a 3d metal surface layer on Pt(111)moves the d-band center closer to the Fermi level as compared to the bulk 3d metals. This is primarily due to the tensile strain induced by the Pt lattice as the ligand effect is the weakest between late transition metal over layers and the Pt(111) substrate [17] . Conversely, subsurface 3d metals shift the surface d-band center of Pt away from the Fermi level as compared to that of Pt(111), mainly due to the electronic interactionof Pt and the subsurface 3d atoms [17] . The comparison in Fig.3A demonstrates that HBE typically follows the trend of 3d-Pt-Pt(111)>Pt(111)> Pt-3d-Pt(111). In addition, the nearly linear correlation between HBE and the surface d-band center should enable one to predict HBE on other bimetallic surfaces based on the extensive database of d-band center values for many bimetallic surfaces [5] .In addition to the trend in the correlation of HBE with surface d-band center, the binding energies of unsaturated hydrocarbons, such as cyclohexene, follows the same trend as HBE. As shown in Fig.3B, DFT calculations reveal that the Pt-3d-Pt(111) subsurface structures bond to cyclohexene more weakly than Pt(111) and the corresponding 3d-Pt-Pt(111) surface structures [18] . For example, DFT results indicate that both cyclohexene and atomic hydrogen are more weakly bonded on Pt-Ni-Pt(111) than on Ni-Pt-Pt(111), Pt(111) and Ni(111), suggesting that the subsurface Pt-Ni-Pt(111) structure should be more effective in the hydrogenation of cyclohexene than the surface structure. This has been confirmed experimentally by comparing the hydrogenation activity of cyclohexene using temperature programmed desorption (TPD), as shown in Fig.4 [18] . As illustrated in the TPD peak area of the cyclohexane product, the subsurface Pt-Ni-Pt (111) structure shows the highest hydrogenation yield, with the desorption peak centered at a very low temperature of 203 K. Similar bimetallic surface structure can also be produced by depositing one monolayer of Pt on a Ni(111) substrate, which also possesses the novel low-temperature pathway for cyclohexene hydrogenation [21] .The trend in the DFT calculations in Fig.3B also shows that the binding energy of cyclohexene on Pt-Co-Pt(111) and Pt-Fe-Pt(111) is even weaker than that on Pt-Ni-Pt(111). Although this might suggest that the former two surfaces would be more active toward the hydrogenation than Pt-Ni-Pt(111), one should keep in mind that the adsorption of cyclohexene needs to be strong enough for the hydrogenation to take place. One would therefore expect to observe a volcano relationship for the hydrogenation activity as the d-band center moves further away from the Fermi level, i.e., when the adsorption of cyclohexene becomes too weak for the hydrogenation to occur. This is verified experimentally in the results shown in Fig.5. The hydrogenation yield from TPD measurements is the highest on Pt-Ni-Pt(111), but starts to decrease on the Pt-Co-Pt(111) and Pt-Fe-Pt(111) surfaces, where the binding of cyclohexene becomes too weak for hydrogenation to occur. On the other side of the volcano curve, the binding energies of cyclohexene on the 3d-Pt-Pt(111) surfaces are too strong, preventing the effective hydrogenation of cyclohexene [18] 2.2 Polycrystalline bimetallic surfacesIndustrial catalysts are often supported nanoparticles of varying shape and size. Polycrystalline bimetallic films provide a potential way to bridge the “materials gap”between single crystal surfaces and supported catalysts. As illustrated in Fig.6, it is possible to assume that the surface chemistry of the nanoparticle should be dominated primarily by the first few atomic layers. It is also reasonable to assume that the chemistry of the individual crystal facets on the nanoparticle (primarily (111) and (100) for an FCC nanoparticle) can be approximated by their respective single crystal extension [22] .With these assumptions we have investigated the chemical properties of 3d-Pt bimetallic structures prepared on a polycrystalline Pt film that contained mainly the (111) and (100) facets. Similar to Pt(111), monolayer Ni was deposit on a Pt foil at room temperature to produce the Ni-Pt-Pt surface structure, followed by annealing to higher temperatures to obtain the Pt-Ni-Pt subsurface structure [22] . The TPD results of the hydrogenation of cyclohexene are shown inFig.7. Similar to the corresponding single crystal surfaces, the subsurface Pt-Ni-Pt polycrystalline structure shows significantly higher hydrogenation activity than that from the polycrystalline Pt and Ni surfaces. These results confirm the assumption that the trend observed on single crystal bimetallic surfaces can be extended to the polycrystalline counterparts.2.3 Thermodynamic stability of bimetallic surfaces under hydrogenation conditionsBefore extending the surface science results to supported catalysts, it is important to verify that the desirable Pt-Ni-Pt subsurface structure is the thermodynamically preferred configuration under hydrogenation conditions. As demonstrated in several recent studies, including single crystal surfaces [23-24] , polycrystalline films [22,25] and supported catalysts [26] , the thermodynamically preferred Pt-Ni bimetallic structure is directly related to the chemical environment present on the surface. Fig.8 shows the DFT predicted potential for segregation for a 3d metal atom to segregate from the subsurface to the surface of Pt(111). These values were calculated for the environments of vacuum, and with 0.5 monolayer (ML) atomic hydrogen and 0.5 ML atomic oxygen, using procedures described previously [24] . The thermodynamic potential for segregation is defined as follows:where ΔE seg is the thermodynamic potential for segregation per Pt-3d pair, E A/3d-Pt-Pt is the total energy for the surface configuration with adsorbate A, E A/Pt-3d-Pt is the total energy for the subsurface configuration with adsorbate A, and M is the total number of Pt-3d pairs per unit cell. As defined in a previous publication [22] , a positive ΔE seg value indicates that the subsurface Pt-3d-Pt is more stable. The DFT results in Fig.8 predict that for the reducing environment of vacuum and 0.5 ML atomic hydrogen, the subsurface configuration is thermodynamically preferred, whereas in 0.5 ML atomic oxygen the surface configuration is preferred. There is a nearly linear trend between ΔE seg and the difference in d-band, ΔƐd , which leads to a generalized equation in predicting the thermodynamic stability of a wide range of bimetallic surfaces [24] . Because the environment of hydrogenation reactions is similar to that of the reducing environment, with the bimetallic surface being partially covered by hydrogen, the results in Fig.8 suggest that the desirable subsurface Pt-Ni-Pt configuration should be thermodynamically stable, making it possible to extend model surfaces to supported catalysts for hydrogenation reactions.2.4 Synthesis and evaluation of supported catalystsSupported monometallic Pt and bimetallic Ni-Pt and Co-Pt catalysts were synthesized on γ-Al 2 O 3 using the incipient wetness method [27-28] . The catalysts were characterized using a variety of techniques, including extended X-ray absorption fine structure (EXAFS). The utilization of EXAFS is critical in these studies because it provides direct information on the extent of bimetallic bond formation based on the coordination numbers of Ni-Pt and Co-Pt under in-situ reaction conditions. As summarized in Table 1, the detection of the Ni-Pt and Co-Pt nearest neighbors confirms that bimetallic bonds are indeed produced on the supported catalysts [28] . The supported catalysts were evaluated using both batch and flow reactors to determine the reaction kinetics of the hydrogenation of cyclohexene at a low temperature of 303 K [28] . Fig.9 shows the production of cyclohexane from cyclohexene on Pt/γ-Al 2 O 3 , Co-Pt/γ-Al 2 O 3 , and Ni-Pt/γ-Al 2 O 3 , using a batch reactor equipped with Fourier transform infrared (FTIR)spectroscopy. The solid lines are fittings using the Langmuir-Hinshelwood model, resulting in a rate constant of 1.7, 21 and 24 min -1 for supported Pt, Co-Pt, and Ni-Pt, respectively [28] . The trend observed in the rate constant of cyclohexene hydrogenation is consistent with that from the single crystal surfaces for the same reaction, Ni-Pt>Co-Pt>Pt, as shown earlier in the volcano curve in Fig.5. The observation of the similar trend between model surfaces and supported catalysts provides an important demonstration of the rational design of bimetallic catalysts from combined theoretical and experimental approaches.3 Research opportunities in bimetallic catalysis3.1 Low-temperature hydrogenation reactionsWe have applied similar combined approaches for the design of bimetallic catalysts for the low-temperature hydrogenation of several types of hydrocarbon molecules. Below we will provide several examples of hydrogenation reactions that are of both fundamental and practical importance.Hydrogenation of acrolein. Studies of the selective hydrogenation of unsaturated aldehydes, such as αβ-unsaturated aldehydes, have been of growing interest for the production of fine chemical sand pharmaceutical precursors [29] .The hydrogenation of the C=C and/or C=O bonds in unsaturated aldehydes offers the possibility to improve both the hydrogenation activity and selectivity through the formation of bimetallic surfaces. Using the hydrogenation of acrolein as a probe reaction, we demonstrated that the selective hydrogenation of the C=O bond can be achieved through the formation of the subsurface Pt-Ni-Pt(111) and Pt-Co-Pt(111) bimetallic structures [30-31] .Hydrogenation of benzene.The hydrogenation of benzene to cyclohexane is of significant importance in the petroleum industry and for environmental protection. The process of benzene hydrogenation has been utilized commercially for the production of cyclohexane, which is one of the key intermediates in the synthesis of Nylon-6 and Nylon-66 [32] . We have identified Co-Pt bimetallic catalysts as promising materials for the hydrogenation of benzene at a relatively low temperature of 343 K [28,33] . For example, Table 2 summarizes the batch and flow reactor results of benzene hydrogenation on several Co-based bimetallic catalysts. The Co-Pt catalyst shows the highest rate constant and lowest activation barrier for the hydrogenation of benzene, which is consistent with the relatively weak binding energies of atomic hydrogen and benzene from DFT calculations [33] . In addition, the catalyst support also plays a role in controlling the hydrogenation activity of Co-Pt catalysts [34]Selective hydrogenation of acetylene in ethylene. The selective hydrogenation of acetylene in the presence of ethylene is an important reaction because acetylene poisons the catalysts in ethylene polymerization reactions [35-36] . By supporting Pd-Ag bimetallic catalysts on ion-exchanged β-zeolites, we observed a synergistic effect that led to a higher selectivity for acetylene hydrogenation in the presence of excess ethylene [37] .The increase in the hydrogenation selectivity is attributed to a combination of an enhanced π-cation interaction between acetylene and zeolite at low temperatures and the ability of the Pd-Ag bimetallic catalysts to perform hydrogenation at such low temperatures [37] .3.2 Reducing bulk Pt in bimetallic catalysts with metal carbidesAs demonstrated in Figs.3-5, the subsurface Pt-Ni-Pt structure is desirable to enhance the activity and selectivity of the hydrogenation of unsaturated hydrocarbons. However, if elevated temperatures are required for reactions, the subsurface Ni atoms start to diffuse into bulk Pt,leaving a monometallic Pt surface and therefore the disappearance of the enhanced bimetallic hydrogenation activity [17,22] . In addition, as shown in Fig.8, adsorbates such as oxygen can cause the subsurface Ni atoms to segregate to the surface, forming the Ni-Pt-Pt surface that is not active for hydrogenation reactions. One idea to overcome such inherent instability of Pt-Ni-Pt is to replace the bulk Pt with an alternative substrate, such as transition metal carbides that often show catalytic properties similar to Pt [38-44] . We have explored the utilization of tungsten monocarbide (WC) to produce the Pt-Ni-WC structure [45] . As WC has been shown to be an effective diffusion barrier layer [46] , thermal deactivation due to Ni diffusion will be alleviated. Furthermore, it is also possible that the WC substrate would anchor Ni by the formation of W—Ni or C—Ni bonds to prevent its segregation to the surface in an oxygen-rich environment. Fig.10 shows a comparison of the hydrogenation of cyclohexene from Pt-Ni-Pt and Pt-Ni-WC surfaces [45] . The Pi-Ni-WC surface shows higher hydrogenation activity, which is consistent with parallel DFT calculations [45] . The promising results in Fig.10 suggest the possibility to synthesize a more active and stable Pt-Ni-WC hydrogenation catalyst with much lower loading of Pt than that in Pt-Ni-Pt.3.3 Production of hydrogen using bimetallic catalystsAll examples presented above are for hydrogenation reactions, which can be classified as hydrogen-consuming reactions and require catalysts to bond to atomic hydrogen and adsorbates relatively weakly. Using the surface d-band center argument in Fig.3, these reactions are preferred on bimetallic catalysts with surface d-band center away from the Fermi level, such as the Pt-3d-Pt subsurface structures. In contrast, for hydrogen-producing reactions, the desirable catalysts should be those that bond to hydrogen and adsorbates more strongly, i.e., with d-band center closer to the Fermi level, such as the 3d-Pt-Pt surface structures. This hypothesis has been confirmed experimentally in our recent studies for the production of H 2 from the reforming of biomass derived molecules, including ethanol, ethylene glycol and glycerol on the Ni-Pt-Pt(111) surface [47] . Similarly, the Ni-Pt-Pt(111) surface also shows a very high activity for H 2 production from the decomposition of ammonia [48] .We have also demonstrated that coking of the catalyst surfaces, a common deactivation mechanism in dehydrogenation reactions, can be reduced by the formation of bimetallic surfaces [49] . These results further demonstrate the possibility of designing bimetallic catalysts from combined theoretical predictions and experimental verification.4 ConclusionsThe field of catalysis is undergoing a revolution in the selection process of catalytic materials, from the traditional“trial-And-error”method to the“rational design”approach, with the latter requiring atomic level understanding of the catalyst structures and reaction mechanisms. In the current review we utilized several hydrogenation reactions to demonstrate the importance of combining theoretical and experimental approaches for designing bimetallic structures with desirable catalytic properties. In addition, the examples also illustrated the possibility to bridge the “materials gap”and “pressure gap”between fundamental studies on single crystal surfaces and catalytic evaluation of supported catalysts. Similar approaches can be adopted for the rational design of bimetallic catalysts beyond hydrogenation reactions.多相催化领域，特别是在催化作用双金属合金，在过去的几十年里看到了许多进展。

AB实验的⾼端玩法系列1-AB实验⼈群定向个体效果差异HTEUpliftModel论⽂gi。

⼀直以来机器学习希望解决的⼀个问题就是'what if'，也就是决策指导：如果我给⽤户发优惠券⽤户会留下来么？如果患者服了这个药⾎压会降低么？如果APP增加这个功能会增加⽤户的使⽤时长么？如果实施这个货币政策对有效提振经济么？这类问题之所以难以解决是因为ground truth在现实中是观测不到的，⼀个已经服了药的患者⾎压降低但我们⽆从知道在同⼀时刻如果他没有服药⾎压是不是也会降低。

这个时候做分析的同学应该会说我们做AB实验！我们估计整体差异，显著就是有效，不显著就是⽆效。

但我们能做的只有这些么？当然不是！因为每个个体都是不同的！整体⽆效不意味着局部群体⽆效！如果只有5%的⽤户对发优惠券敏感，我们能只触达这些⽤户么？或者不同⽤户对优惠券敏感的阈值不同，如何通过调整优惠券的阈值吸引更多的⽤户？如果降压药只对有特殊症状的患者有效，我们该如何找到这些患者？APP的新功能部分⽤户不喜欢，部分⽤户很喜欢，我能通过⽐较这些⽤户的差异找到改进这个新功能的⽅向么？以下⽅法从不同的⾓度尝试解决这个问题，但基本思路是⼀致的：我们⽆法观测到每个⽤户的treatment effect,但我们可以找到⼀群相似⽤户来估计实验对他们的影响。

我会在之后的博客中，从CasualTree的第⼆篇Recursive partitioning for heterogeneous causal effects开始梳理下述⽅法中的异同。

整个领域还在发展中，⼏个开源代码都刚release不久，所以这个博客也会持续更新。

如果⼤家看到好的⽂章和⼯程实现也欢迎在下⾯评论～Uplift Modelling/Causal Tree1. Nicholas J Radcliffe and Patrick D Surry. Real-world uplift modelling with significance based uplift trees. White Paper TR-2011-1,Stochastic Solutions, 2011.2. Rzepakowski, P. and Jaroszewicz, S., 2012. Decision trees for uplift modeling with single and multiple treatments. Knowledge andInformation Systems, 32(2), pp.303-327.3. Yan Zhao, Xiao Fang, and David Simchi-Levi. Uplift modeling with multiple treatments and general response types. Proceedings ofthe 2017 SIAM International Conference on Data Mining, SIAM, 2017.4. Athey, S., and Imbens, G. W. 2015. Machine learning methods forestimating heterogeneous causal effects. stat 1050(5)5. Athey, S., and Imbens, G. 2016. Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy ofSciences.6. C. Tran and E. Zheleva, “Learning triggers for heterogeneous treatment effects,” in Proceedings of the AAAI Conference on ArtificialIntelligence, 2019Forest Based Estimators1. Wager, S. & Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using random forests. Journal of theAmerican Statistical Association .2. M. Oprescu, V. Syrgkanis and Z. S. Wu. Orthogonal Random Forest for Causal Inference. Proceedings of the 36th InternationalConference on Machine Learning (ICML), 2019Double Machine Learning1. V. Chernozhukov, D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, and a. W. Newey. Double Machine Learning for Treatment andCausal Parameters. ArXiv e-prints2. V. Chernozhukov, M. Goldman, V. Semenova, and M. Taddy. Orthogonal Machine Learning for Demand Estimation: HighDimensional Causal Inference in Dynamic Panels. ArXiv e-prints, December 2017.3. V. Chernozhukov, D. Nekipelov, V. Semenova, and V. Syrgkanis. Two-Stage Estimation with a High-Dimensional Second Stage.2018.4. X. Nie and S. Wager. Quasi-Oracle Estimation of Heterogeneous Treatment Effects. arXiv preprint arXiv:1712.04912, 2017.5. D. Foster and V. Syrgkanis. Orthogonal Statistical Learning. arXiv preprint arXiv:1901.09036, 2019Meta Learner1. C. Manahan, 2005. A proportional hazards approach to campaign list selection. In SAS User Group International (SUGI) 30Proceedings.2. Green DP, Kern HL (2012) Modeling heteroge-neous treatment effects in survey experiments with Bayesian additive regression trees.Public OpinionQuarterly 76(3):491–511.3. Sören R. Künzel, Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. Metalearners for estimating heterogeneous treatment effects usingmachine learning. Proceedings of the National Academy of Sciences, 2019.Deep Learning1. Fredrik D. Johansson, U. Shalit, D. Sontag.ICML (2016). Learning Representations for Counterfactual Inference2. Shalit, U., Johansson, F. D., & Sontag, D. ICML (2017). Estimating individual treatment effect: generalization bounds and algorithms.Proceedings of the 34th International Conference on Machine Learning3. Christos Louizos, U. Shalit, J. Mooij, D. Sontag, R. Zemel, M. Welling.NIPS (2017). Causal Effect Inference with Deep Latent-VariableModels4. Alaa, A. M., Weisz, M., & van der Schaar, M. (2017). Deep Counterfactual Networks with Propensity-Dropout5. Shi, C., Blei, D. M., & Veitch, V. NeurIPS (2019). Adapting Neural Networks for the Estimation of Treatment EffectsUber专场最早就是uber的博客在茫茫paper的海洋中帮我找到了⽅向，如今听说它们AI LAB要解散了有些伤感，作为HTE最多star的开源⽅，它们值得拥有⼀个part1. Shuyang Du, James Lee, Farzin Ghaffarizadeh, 2017, Improve User Retention with Causal Learning2. Zhenyu Zhao, Totte Harinen, 2020, Uplift Modeling for Multiple Treatments with Cost3. Will Y. Zou, Smitha Shyam, Michael Mui, Mingshi Wang, 2020, Learning Continuous Treatment Policy and Bipartite Embeddings forMatching with Heterogeneous Causal EffectsOptimization4. Will Y. Zou,Shuyang Du,James Lee,Jan Pedersen, 2020, Heterogeneous Causal Learning for Effectiveness Optimizationin User Marketing想看更多因果推理AB实验相关paper的⼩伙伴看过来持续更新中 ~。