Pub Date : 2023-07-27DOI: 10.1142/s2010324723500261
H. Eraki, N. Saber, Z. Fadil, A. Mhirech, B. Kabouchi, L. Bahmad
{"title":"Monte Carlo Study on the Magnetic Properties of the Silicene-Germanene Junction-Like Nanostructure","authors":"H. Eraki, N. Saber, Z. Fadil, A. Mhirech, B. Kabouchi, L. Bahmad","doi":"10.1142/s2010324723500261","DOIUrl":"https://doi.org/10.1142/s2010324723500261","url":null,"abstract":"","PeriodicalId":54319,"journal":{"name":"Spin","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45042964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.1142/s201032472350025x
M. Mouhib, S. Bri, M. D. Belrhiti, H. Mounir
{"title":"Study of the Mixed Spin Ising Nanowire with Random and Dilute Crystal Field","authors":"M. Mouhib, S. Bri, M. D. Belrhiti, H. Mounir","doi":"10.1142/s201032472350025x","DOIUrl":"https://doi.org/10.1142/s201032472350025x","url":null,"abstract":"","PeriodicalId":54319,"journal":{"name":"Spin","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48281740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-21DOI: 10.1142/s2010324723400192
P. Gupta, B. Singh, A. Mishra, Aditya Kumar, A. Sarkar, M. Waschk, S. Bedanta
{"title":"Tailoring spin to charge conversion efficiency via microwave frequency in La0.67Sr0.33MnO3/Pt bilayer system","authors":"P. Gupta, B. Singh, A. Mishra, Aditya Kumar, A. Sarkar, M. Waschk, S. Bedanta","doi":"10.1142/s2010324723400192","DOIUrl":"https://doi.org/10.1142/s2010324723400192","url":null,"abstract":"","PeriodicalId":54319,"journal":{"name":"Spin","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47143225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-21DOI: 10.1142/s2010324723500236
Dongyan Zhao, Yanning Chen, Zanhong Chen, Cheng Pan, Jin Shao, A. Du, W. Cai, K. Cao, Z. Fu, Kewen Shi
{"title":"Magnetization switching in atom-thick Mo engineered Exchange bias-based SOT-MRAM","authors":"Dongyan Zhao, Yanning Chen, Zanhong Chen, Cheng Pan, Jin Shao, A. Du, W. Cai, K. Cao, Z. Fu, Kewen Shi","doi":"10.1142/s2010324723500236","DOIUrl":"https://doi.org/10.1142/s2010324723500236","url":null,"abstract":"","PeriodicalId":54319,"journal":{"name":"Spin","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45832025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-18DOI: 10.48550/arXiv.2307.08979
Quanquan C. Liu, Yiduo Ke, S. Khuller
In this paper, we give new auction algorithms for maximum weighted bipartite matching (MWM) and maximum cardinality bipartite $b$-matching (MCbM). Our algorithms run in $Oleft(log n/varepsilon^8right)$ and $Oleft(log n/varepsilon^2right)$ rounds, respectively, in the blackboard distributed setting. We show that our MWM algorithm can be implemented in the distributed, interactive setting using $O(log^2 n)$ and $O(log n)$ bit messages, respectively, directly answering the open question posed by Demange, Gale and Sotomayor [DNO14]. Furthermore, we implement our algorithms in a variety of other models including the the semi-streaming model, the shared-memory work-depth model, and the massively parallel computation model. Our semi-streaming MWM algorithm uses $O(1/varepsilon^8)$ passes in $O(n log n cdot log(1/varepsilon))$ space and our MCbM algorithm runs in $O(1/varepsilon^2)$ passes using $Oleft(left(sum_{i in L} b_i + |R|right)log(1/varepsilon)right)$ space (where parameters $b_i$ represent the degree constraints on the $b$-matching and $L$ and $R$ represent the left and right side of the bipartite graph, respectively). Both of these algorithms improves emph{exponentially} the dependence on $varepsilon$ in the space complexity in the semi-streaming model against the best-known algorithms for these problems, in addition to improvements in round complexity for MCbM. Finally, our algorithms eliminate the large polylogarithmic dependence on $n$ in depth and number of rounds in the work-depth and massively parallel computation models, respectively, improving on previous results which have large polylogarithmic dependence on $n$ (and exponential dependence on $varepsilon$ in the MPC model).
本文给出了最大加权二部匹配(MWM)和最大基数二部$b$匹配(MCbM)的拍卖算法。我们的算法分别以$Oleft(log n/varepsilon^8right)$和$Oleft(log n/varepsilon^2right)$轮在黑板分布式设置中运行。我们表明，我们的MWM算法可以分别使用$O(log^2 n)$和$O(log n)$位消息在分布式、交互式设置中实现，直接回答了Demange、Gale和Sotomayor [DNO14]提出的开放性问题。此外，我们还在各种其他模型中实现了我们的算法，包括半流模型、共享内存工作深度模型和大规模并行计算模型。我们的半流MWM算法在$O(n log n cdot log(1/varepsilon))$空间中使用$O(1/varepsilon^8)$通道，而我们的MCbM算法使用$Oleft(left(sum_{i in L} b_i + |R|right)log(1/varepsilon)right)$空间在$O(1/varepsilon^2)$通道中运行(其中参数$b_i$表示$b$匹配的程度约束，$L$和$R$分别表示二部图的左侧和右侧)。除了改进MCbM的轮复杂度外，这两种算法还emph{以}指数方式提高了半流模型中对$varepsilon$的空间复杂度的依赖，而不是针对这些问题的最知名算法。最后，我们的算法在工作深度和大规模并行计算模型中分别消除了对$n$深度和轮数的大量多对数依赖，改进了先前对$n$有大量多对数依赖的结果(以及MPC模型中对$varepsilon$的指数依赖)。
{"title":"Scalable Auction Algorithms for Bipartite Maximum Matching Problems","authors":"Quanquan C. Liu, Yiduo Ke, S. Khuller","doi":"10.48550/arXiv.2307.08979","DOIUrl":"https://doi.org/10.48550/arXiv.2307.08979","url":null,"abstract":"In this paper, we give new auction algorithms for maximum weighted bipartite matching (MWM) and maximum cardinality bipartite $b$-matching (MCbM). Our algorithms run in $Oleft(log n/varepsilon^8right)$ and $Oleft(log n/varepsilon^2right)$ rounds, respectively, in the blackboard distributed setting. We show that our MWM algorithm can be implemented in the distributed, interactive setting using $O(log^2 n)$ and $O(log n)$ bit messages, respectively, directly answering the open question posed by Demange, Gale and Sotomayor [DNO14]. Furthermore, we implement our algorithms in a variety of other models including the the semi-streaming model, the shared-memory work-depth model, and the massively parallel computation model. Our semi-streaming MWM algorithm uses $O(1/varepsilon^8)$ passes in $O(n log n cdot log(1/varepsilon))$ space and our MCbM algorithm runs in $O(1/varepsilon^2)$ passes using $Oleft(left(sum_{i in L} b_i + |R|right)log(1/varepsilon)right)$ space (where parameters $b_i$ represent the degree constraints on the $b$-matching and $L$ and $R$ represent the left and right side of the bipartite graph, respectively). Both of these algorithms improves emph{exponentially} the dependence on $varepsilon$ in the space complexity in the semi-streaming model against the best-known algorithms for these problems, in addition to improvements in round complexity for MCbM. Finally, our algorithms eliminate the large polylogarithmic dependence on $n$ in depth and number of rounds in the work-depth and massively parallel computation models, respectively, improving on previous results which have large polylogarithmic dependence on $n$ (and exponential dependence on $varepsilon$ in the MPC model).","PeriodicalId":54319,"journal":{"name":"Spin","volume":"130 1","pages":"28:1-28:24"},"PeriodicalIF":1.8,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76861549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-15DOI: 10.48550/arXiv.2307.07727
Charilaos Efthymiou, Thomas P. Hayes, Daniel Stefankovic, Eric Vigoda
We study the mixing time of the single-site update Markov chain, known as the Glauber dynamics, for generating a random independent set of a tree. Our focus is obtaining optimal convergence results for arbitrary trees. We consider the more general problem of sampling from the Gibbs distribution in the hard-core model where independent sets are weighted by a parameter $lambda>0$. Previous work of Martinelli, Sinclair and Weitz (2004) obtained optimal mixing time bounds for the complete $Delta$-regular tree for all $lambda$. However, Restrepo et al. (2014) showed that for sufficiently large $lambda$ there are bounded-degree trees where optimal mixing does not hold. Recent work of Eppstein and Frishberg (2022) proved a polynomial mixing time bound for the Glauber dynamics for arbitrary trees, and more generally for graphs of bounded tree-width. We establish an optimal bound on the relaxation time (i.e., inverse spectral gap) of $O(n)$ for the Glauber dynamics for unweighted independent sets on arbitrary trees. Moreover, for $lambdaleq .44$ we prove an optimal mixing time bound of $O(nlog{n})$. We stress that our results hold for arbitrary trees and there is no dependence on the maximum degree $Delta$. Interestingly, our results extend (far) beyond the uniqueness threshold which is on the order $lambda=O(1/Delta)$. Our proof approach is inspired by recent work on spectral independence. In fact, we prove that spectral independence holds with a constant independent of the maximum degree for any tree, but this does not imply mixing for general trees as the optimal mixing results of Chen, Liu, and Vigoda (2021) only apply for bounded degree graphs. We instead utilize the combinatorial nature of independent sets to directly prove approximate tensorization of variance/entropy via a non-trivial inductive proof.
{"title":"Optimal Mixing via Tensorization for Random Independent Sets on Arbitrary Trees","authors":"Charilaos Efthymiou, Thomas P. Hayes, Daniel Stefankovic, Eric Vigoda","doi":"10.48550/arXiv.2307.07727","DOIUrl":"https://doi.org/10.48550/arXiv.2307.07727","url":null,"abstract":"We study the mixing time of the single-site update Markov chain, known as the Glauber dynamics, for generating a random independent set of a tree. Our focus is obtaining optimal convergence results for arbitrary trees. We consider the more general problem of sampling from the Gibbs distribution in the hard-core model where independent sets are weighted by a parameter $lambda>0$. Previous work of Martinelli, Sinclair and Weitz (2004) obtained optimal mixing time bounds for the complete $Delta$-regular tree for all $lambda$. However, Restrepo et al. (2014) showed that for sufficiently large $lambda$ there are bounded-degree trees where optimal mixing does not hold. Recent work of Eppstein and Frishberg (2022) proved a polynomial mixing time bound for the Glauber dynamics for arbitrary trees, and more generally for graphs of bounded tree-width. We establish an optimal bound on the relaxation time (i.e., inverse spectral gap) of $O(n)$ for the Glauber dynamics for unweighted independent sets on arbitrary trees. Moreover, for $lambdaleq .44$ we prove an optimal mixing time bound of $O(nlog{n})$. We stress that our results hold for arbitrary trees and there is no dependence on the maximum degree $Delta$. Interestingly, our results extend (far) beyond the uniqueness threshold which is on the order $lambda=O(1/Delta)$. Our proof approach is inspired by recent work on spectral independence. In fact, we prove that spectral independence holds with a constant independent of the maximum degree for any tree, but this does not imply mixing for general trees as the optimal mixing results of Chen, Liu, and Vigoda (2021) only apply for bounded degree graphs. We instead utilize the combinatorial nature of independent sets to directly prove approximate tensorization of variance/entropy via a non-trivial inductive proof.","PeriodicalId":54319,"journal":{"name":"Spin","volume":"38 1","pages":"33:1-33:16"},"PeriodicalIF":1.8,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74035066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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