87,116 research outputs found
Correspondence, Charles Cockburn -- 1974 -- OPV WHO -- letter, 1974-02-07
No full textLetter from Assaad, F. to Sabin, Albert B. dated 1974-02-07.Sabin Collection Fair Use Policy</a
Ahlam et les éboueurs du Caire, F. Assaad
No full textASSAAD, Fawziya. Ahlam et les éboueurs du Caire. Ed. L'Hèbe, 2004 C'est l'histoire d'Ahlam et de son peuple : paysans chrétiens fuyant la misère de la campagne dans les détritus du Caire, ils deviendront porchers élevant les bêtes interdites aux musulmans et aux juifs, vivront dans des pyramides de déchets qu'ils trient, brûlent, recyclent. C'est l'histoire des miséreux qui s'organisent, grâce au regard que pose sur eux Soeur Emmanuelle d'abord, la communauté internationale ensuite ; l'histo..
Phases and exotic phase transitions of a two-dimensional Su-Schrieffer-Heeger model
No full textData to reproduce the figures of the publication: A. Götz, M. Hohenadler, and F. F. Assaad, Phases and exotic phase transitions of a two-dimensional Su-Schrieffer-Heeger model, Phys. Rev. B 109, 195154 (2024). Please read the 'README' file.We thank the Würzburg-Dresden Cluster of Excellence on Complexity and Topology in Quantum Matter ct.qmat (EXC 2147, Project No. 390858490) as well as the DFG under the grant AS 120/16- 1 (Project No. 493886309) that is part of the collaborative research project SFB Q-M&S funded by the Austrian Science Fund (FWF) F 86. We are grateful for funding support from the DFG funded SFB 1170 on Topological and Correlated Electronics at Surfaces and Interfaces under the Grant No. C01. The authors gratefully acknowledge the Gauss Centre for Supercomputing e.V. for funding this project by providing computing time on the GCS Supercomputer SuperMUC-NG at the Leibniz Supercomputing Centre. The authors gratefully acknowledge the scientific support and HPC resources provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) under NHR Project No. 80069. NHR funding is provided by federal and Bavarian state authorities. NHR@FAU hardware is partially funded by the German Research Foundation (DFG) through Grant No. 440719683
Phase diagram of the 1-dimensional t--J mode
No full textThe phase diagram of the one-dimensional t-J model is investigated by analyzing the results of exact diagonalization and the exact solutions at J/t=0 and 2. Phase separation takes place above a critical value of J around Jc/t=2.5 3.5 depending on the electron density. In the small-J region, Tomonaga-Luttinger liquid theory holds and its correlation exponents are calculated as a function of J/t and the electron density. Superconducting correlations become dominant in a region between the solvable case (J/t=2) and phase separation. A spin-gap region is also found at low density. © 1991 The American Physical Society
fork structure with different sampling rate
No full textThis structure is generated by the following equations:
V^{(1)}_t = a_{11}^{t-1} * V^{(1)}_{t - 1} + b_{1}^{t} * \eps^1_t
V^{(2)}_t = a_{22}^{t-1} * V^{(2)}_{t - 1} + a_{12}^{t-1} * f(V^{(1)}_{t - 1}) + b_{2}^{t} * \eps^2_t
V^{(3)}_t = a_{33}^{t-2} * V^{(3)}_{t -2} + a_{13}^{t-2} * g(V^{(1)}_{t - 2}) + b_{3}^{t} * \eps^3_t for t%2 == 0
We present below the characteristics of each dataset in the following form: (f(), g(), a_{11}^{t-1} b_{1}^{t} a_{22}^{t-1} b_{2}^{t} a_{33}^{t-1} b_{3}^{t} a_{12}^{t-1} a_{13}^{t-2})
Dataset 1: (abso, tanh, -0.3699361331241453 0.1 0.3161208615068922 0.1 -0.2363490322338957 0.1 -0.22880097522132448 0.24306200315342608)
Dataset 2: (tanh, tanh, -0.45088901737058884 0.1 -0.5049523437439734 0.1 -0.23912014762598788 0.1 -0.10325271020569993 0.7873766287095652)
Dataset 3: (tanh, sin, 0.7930103819840089 0.1 0.19504243966386414 0.1 0.8840652564435134 0.1 0.6629027828151408 0.23547876432744697)
Dataset 4: (abso, tanh, 0.7943275308593616 0.1 -0.7759848046608431 0.1 -0.623677017045875 0.1 -0.7383399199106677 -0.11855529995628933)
Dataset 5: (sin, sin, 0.256086048358537 0.1 -0.14477078888448647 0.1 0.30103146916183454 0.1 -0.9673057274489223 0.5287051820012552)
Dataset 6: (sin, abso, 0.6747045268600222 0.1 0.649657313651451 0.1 0.7784498897392678 0.1 0.38390606423309004 -0.9649074283617511)
Dataset 7: (cos, abso, 0.14573687608102737 0.1 -0.7059698692397858 0.1 -0.3245043416841211 0.1 -0.5811576720181868 -0.8263972836584903)
Dataset 8: (abso, abso, 0.5284812027494568 0.1 -0.1725023723393413 0.1 0.45721703518389356 0.1 -0.6942436019409397 0.7077795117202967)
Dataset 9: (tanh, cos, 0.41729031657734783 0.1 -0.21854259329506664 0.1 0.20616273064126855 0.1 -0.7797294278325508 -0.1260612794202709)
Dataset 10: (cos, cos, 0.6125159746673405 0.1 -0.7233943050608049 0.1 -0.13702376727771082 0.1 -0.14928558123954971 -0.11666721635492361
fork structure
No full textThis structure is generated by the following equations:
V^{(1)}_t = a_{11}^{t-1} * V^{(1)}_{t - 1} + b_{1}^{t} * \eps^1_t
V^{(2)}_t = a_{22}^{t-1} * V^{(2)}_{t - 1} + a_{12}^{t-1} * f(V^{(1)}_{t - 1}) + b_{2}^{t} * \eps^2_t
V^{(3)}_t = a_{33}^{t-1} * V^{(3)}_{t - 1} + a_{13}^{t-2} * g(V^{(1)}_{t - 2}) + b_{3}^{t} * \eps^3_t
We present below the characteristics of each dataset in the following form: (f(), g(), a_{11}^{t-1} b_{1}^{t} a_{22}^{t-1} b_{2}^{t} a_{33}^{t-1} b_{3}^{t} a_{12}^{t-1} a_{13}^{t-2})
Dataset 1: (abso, tanh, -0.3699361331241453 0.1 0.3161208615068922 0.1 -0.2363490322338957 0.1 -0.22880097522132448 0.24306200315342608)
Dataset 2: (tanh, tanh, -0.45088901737058884 0.1 -0.5049523437439734 0.1 -0.23912014762598788 0.1 -0.10325271020569993 0.7873766287095652)
Dataset 3: (tanh, sin, 0.7930103819840089 0.1 0.19504243966386414 0.1 0.8840652564435134 0.1 0.6629027828151408 0.23547876432744697)
Dataset 4: (abso, tanh, 0.7943275308593616 0.1 -0.7759848046608431 0.1 -0.623677017045875 0.1 -0.7383399199106677 -0.11855529995628933)
Dataset 5: (sin, sin, 0.256086048358537 0.1 -0.14477078888448647 0.1 0.30103146916183454 0.1 -0.9673057274489223 0.5287051820012552)
Dataset 6: (sin, abso, 0.6747045268600222 0.1 0.649657313651451 0.1 0.7784498897392678 0.1 0.38390606423309004 -0.9649074283617511)
Dataset 7: (cos, abso, 0.14573687608102737 0.1 -0.7059698692397858 0.1 -0.3245043416841211 0.1 -0.5811576720181868 -0.8263972836584903)
Dataset 8: (abso, abso, 0.5284812027494568 0.1 -0.1725023723393413 0.1 0.45721703518389356 0.1 -0.6942436019409397 0.7077795117202967)
Dataset 9: (tanh, cos, 0.41729031657734783 0.1 -0.21854259329506664 0.1 0.20616273064126855 0.1 -0.7797294278325508 -0.1260612794202709)
Dataset 10: (cos, cos, 0.6125159746673405 0.1 -0.7233943050608049 0.1 -0.13702376727771082 0.1 -0.14928558123954971 -0.11666721635492361
mediator structure
No full text
V^{(1)}_t = a_{11}^{t-1} * V^{(1)}_{t - 1} + b_{1}^{t} * \eps^1_t
V^{(2)}_t = a_{22}^{t-1} * V^{(2)}_{t - 1} + a_{12}^{t-1} * f(V^{(1)}_{t - 1}) + b_{2}^{t} * \eps^2_t
V^{(3)}_t = a_{33}^{t-1} * V^{(3)}_{t - 1} + a_{13}^{t-2} * g(V^{(1)}_{t - 2}) + a_{23}^{t-1} * h(V^{(2)}_{t - 1}) + b_{3}^{t} * \eps^3_t
We present below the characteristics of each dataset in the following form: (f(), g(), h(), a_{11}^{t-1} b_{1}^{t} a_{22}^{t-1} b_{2}^{t} a_{33}^{t-1} b_{3}^{t} a_{12}^{t-1} a_{13}^{t-2} a_{23}^{t-1})
Dataset 1: (abso, abso, cos, 0.4690047938595834 0.1 0.9479395907855845 0.1 0.5268354929774564 0.1 0.48472191233851225 0.6166910968112946 0.4364828031175205)
Dataset 2: (sin, abso, tanh,-0.3358101964117972 0.1 0.5447388334483942 0.1 -0.593586653252502 0.1 -0.6851570258267166 0.40617918107666995 -0.760348443323906)
Dataset 3: (sin, tanh, tanh, -0.3103409580696437 0.1 0.19157917654309542 0.1 0.7201756767517731 0.1 0.14770635555285816 -0.7785931638047321 0.653761128421966)
Dataset 4: (tanh, abso, cos, 0.5660587484803361 0.1 0.47755069621613533 0.1 -0.7429317544856375 0.1 0.4857863582309987 0.9393505225427887 -0.9793254574897177)
Dataset 5: (abos, abso, sin, -0.4515693667452767 0.1 0.8688127262664711 0.1 0.8937925603149124 0.1 0.5178068614229168 0.21166703751962834 -0.4327175549763074)
Dataset 6: (abso, abso, tanh, 0.13789374331826032 0.1 -0.8572497357974211 0.1 -0.6032283124007425 0.1 -0.24780360785385924 0.7176426973103909 -0.746883608238494)
Dataset 7: (tanh, cos, tanh, 0.3419187109634674 0.1 -0.5030340420904331 0.1 -0.31956231453851514 0.1 -0.7011177931845314 0.29670785570237657 0.3665603050569055)
Dataset 8: (tanh, abso, cos, 0.8317248486186986 0.1 -0.8403220343242301 0.1 0.5142907242113686 0.1 -0.8879223103663403 -0.4081907684569219 -0.7027337629717276)
Dataset 9: (tanh, tanh, sin, -0.21433143804375399 0.1 0.7106303179780467 0.1 0.13768409633544243 0.1 0.33008135626490676 -0.4739075499050336 0.22206114361940887)
Dataset 10: (sin, cos, sin, -0.8930925430538972 0.1 -0.554550604752432 0.1 -0.9926898627440199 0.1 0.5123028191188166 0.2582102484789708 -0.23542594375377068
mediator structure with different sampling rate
No full text
V^{(1)}_t = a_{11}^{t-1} * V^{(1)}_{t - 1} + b_{1}^{t} * \eps^1_t
V^{(2)}_t = a_{22}^{t-1} * V^{(2)}_{t - 1} + a_{12}^{t-1} * f(V^{(1)}_{t - 1}) + b_{2}^{t} * \eps^2_t
V^{(3)}_t = a_{33}^{t-2} * V^{(3)}_{t - 2} + a_{13}^{t-2} * g(V^{(1)}_{t - 2}) + a_{23}^{t-1} * h(V^{(2)}_{t - 1}) + b_{3}^{t} * \eps^3_t for t%2==0
We present below the characteristics of each dataset in the following form: (f(), g(), h(), a_{11}^{t-1} b_{1}^{t} a_{22}^{t-1} b_{2}^{t} a_{33}^{t-1} b_{3}^{t} a_{12}^{t-1} a_{13}^{t-2} a_{23}^{t-1})
Dataset 1: (abso, abso, cos, 0.4690047938595834 0.1 0.9479395907855845 0.1 0.5268354929774564 0.1 0.48472191233851225 0.6166910968112946 0.4364828031175205)
Dataset 2: (sin, abso, tanh,-0.3358101964117972 0.1 0.5447388334483942 0.1 -0.593586653252502 0.1 -0.6851570258267166 0.40617918107666995 -0.760348443323906)
Dataset 3: (sin, tanh, tanh, -0.3103409580696437 0.1 0.19157917654309542 0.1 0.7201756767517731 0.1 0.14770635555285816 -0.7785931638047321 0.653761128421966)
Dataset 4: (tanh, abso, cos, 0.5660587484803361 0.1 0.47755069621613533 0.1 -0.7429317544856375 0.1 0.4857863582309987 0.9393505225427887 -0.9793254574897177)
Dataset 5: (abos, abso, sin, -0.4515693667452767 0.1 0.8688127262664711 0.1 0.8937925603149124 0.1 0.5178068614229168 0.21166703751962834 -0.4327175549763074)
Dataset 6: (abso, abso, tanh, 0.13789374331826032 0.1 -0.8572497357974211 0.1 -0.6032283124007425 0.1 -0.24780360785385924 0.7176426973103909 -0.746883608238494)
Dataset 7: (tanh, cos, tanh, 0.3419187109634674 0.1 -0.5030340420904331 0.1 -0.31956231453851514 0.1 -0.7011177931845314 0.29670785570237657 0.3665603050569055)
Dataset 8: (tanh, abso, cos, 0.8317248486186986 0.1 -0.8403220343242301 0.1 0.5142907242113686 0.1 -0.8879223103663403 -0.4081907684569219 -0.7027337629717276)
Dataset 9: (tanh, tanh, sin, -0.21433143804375399 0.1 0.7106303179780467 0.1 0.13768409633544243 0.1 0.33008135626490676 -0.4739075499050336 0.22206114361940887)
Dataset 10: (sin, cos, sin, -0.8930925430538972 0.1 -0.554550604752432 0.1 -0.9926898627440199 0.1 0.5123028191188166 0.2582102484789708 -0.23542594375377068
diamond structure
No full text
V^{(1)}_t = a_{11}^{t-1} * V^{(1)}_{t - 1} + b_{1}^{t} * \eps^1_t
V^{(2)}_t = a_{22}^{t-1} * V^{(2)}_{t - 1} + a_{12}^{t-1} * f(V^{(1)}_{t - 1}) + b_{2}^{t} * \eps^2_t
V^{(3)}_t = a_{33}^{t-1} * V^{(3)}_{t - 1} + a_{13}^{t-2} * g(V^{(1)}_{t - 2}) + b_{3}^{t} * \eps^3_t
V^{(4)}_t = a_{44}^{t-1} * V^{(4)}_{t - 1} + a_{24}^{t-1} * h(V^{(2)}_{t - 1}) + a_{34}^{t-1} * k(V^{(3)}_{t - 1}) + b_{4}^{t} * \eps^4_t
We present below the characteristics of each dataset in the following form: (f(), g(), h(), k(), a_{11}^{t-1} b_{1}^{t} a_{22}^{t-1} b_{2}^{t} a_{33}^{t-1} b_{3}^{t} a_{12}^{t-1} a_{13}^{t-2} a_{24}^{t-1} a_{34}^{t-1})
Dataset 1: (sin, abso, cos, abso, 0.9309567078069418 0.1 -0.778529554582533 0.1 0.2300116194112345 0.1 -0.22296240378428722 0.1 -0.9016841569972256 -0.6896374472205666 0.10106602963375222 0.48008049653674933)
Dataset 2: (sin, abso, cos, sin, -0.6445525445106195 0.1 -0.3780525405462092 0.1 -0.701796066340127 0.1 0.20731717253819482 0.1 0.8131051091268042 -0.890858737148861 0.2653890918892732 0.23697535900073197)
Dataset 3: (tanh, sin, sin, sin, 0.41729031657734783 0.1 -0.21854259329506664 0.1 0.20616273064126855 0.1 -0.7797294278325508 0.1 -0.1260612794202709 0.6310044264068297 -0.18787068647253413 0.5650656324046182)
Dataset 4: (abso, cos, tanh, cos, 0.14478485711277966 0.1 -0.28216629933091975 0.1 -0.26674061963976925 0.1 0.18504798424111368 0.1 -0.7996863438191957 -0.867099397739937 -0.9951612487921924 -0.14521085818905433)
Dataset 5: (cos, tanh, sin, tanh, -0.9837934337769325 0.1 -0.43394819890144753 0.1 0.3884834328535811 0.1 -0.8536098912129784 0.1 0.5213495591191433 0.3165955847380819 0.9091455508726818 0.510568968961665)
Dataset 6: (sin, abso, tanh, abso , 0.33892872454094913 0.1 0.5148639129962742 0.1 -0.4569622735764802 0.1 0.9477074909578327 0.1 0.6377908667571701 -0.46830609659539135 -0.5798139877544422 0.4356478854733741)
Dataset 7: (abso, tanh, sin, sin, 0.5121641023339085 0.1 0.694715274534131 0.1 0.9837227558611266 0.1 -0.3105830731679686 0.1 -0.527918758018074 -0.2370548163098869 -0.2833005908651798 -0.5871738972215943)
Dataset 8: (cos, abso, cos, cos, -0.7061341235944874 0.1 -0.9331272275300622 0.1 -0.18701994675742517 0.1 -0.16023556394146676 0.1 -0.10950368295420954 -0.4933970461539452 -0.6146655566431816 0.7923388730694947)
Dataset 9: (sin, sin, cos, abso, 0.1434182590382893 0.1 0.850704862471533 0.1 0.4613899443716907 0.1 0.7205483099170031 0.1 -0.2720159082576563 0.42184537910443054 -0.13289914190854013 0.9474762557759804)
Dataset 10: (sin, sin, abso, abso, -0.9626878741800082 0.1 -0.9906819486585603 0.1 -0.20280713860380106 0.1 0.14333518073117246 0.1 -0.39449807494635114 -0.7905654574225724 -0.14839637300417796 -0.3380778677752805
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