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The sawtooth chain compound CsCo 2 (MoO 4 ) 2 (OH) is a complex magnetic system and here, we present a comprehensive series of magnetic and neutron scattering measurements to determine its magnetic phase diagram. The magnetic properties of CsCo 2 (MoO 4 ) 2 (OH) exhibit a strong coupling to the crystal lattice and its magnetic ground state can be easily manipulated by applied magnetic fields. There are two unique Co 2+ ions, base and vertex, with J bb and J bv magnetic exchange. The magnetism is highly anisotropic with the b -axis (chain) along the easy axis and the material orders antiferromagnetically at T N = 5 K. There are two successive metamagnetic transitions, the first at H c 1 = 0.2 kOe into a ferrimagnetic structure, and the other at H c 2 = 20 kOe to a ferromagnetic phase. Heat capacity measurements in various fields support the metamagnetic phase transformations, and the magnetic entropy value is intermediate between S = 3/2 and 1/2 states. The zero field antiferromagnetic phase contains vertex magnetic vectors (Co(1)) aligned parallel to the b -axis, while the base vectors (Co(2)) are canted by 34° and aligned in an opposite direction to the vertex vectors. The spins in parallel adjacent chains align in opposite directions, creating an overall antiferromagnetic structure. At a 3 kOe applied magnetic field, adjacent chains flip by 180° to generate a ferrimagnetic phase. An increase in field gradually induces the Co(1) moment to rotate along the b -axis and align in the same direction with Co(2) generating a ferromagnetic structure. The antiferromagnetic exchange parameters are calculated to be J bb = 0.028 meV and J bv = 0.13 meV, while the interchain exchange parameter is considerably weaker at J ch = (0.0047/ N ch ) meV. Our results demonstrate that the CsCo 2 (MoO 4 ) 2 (OH) is a promising candidate to study new physics associated with sawtooth chain magnetism and it encourages further theoretical studies as well as the synthesis of other sawtooth chain structures with different magnetic ions.
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Sketching Meets Differential Privacy: Fast Algorithm for Dynamic Kronecker Projection Maintenance
Projection maintenance is one of the core data structure tasks. Efficient data structures for projection maintenance have led to recent breakthroughs in many convex programming algorithms. In this work, we further extend this framework to the Kronecker product structure. Given a constraint matrix A and a positive semi-definite matrix W∈R^{n×n} with a sparse eigenbasis, we consider the task of maintaining the projection in the form of B^⊤(BB^⊤)^{−1} B, where B=A(W⊗I) or B=A(W^{1/2}⊗W^{1/2}). At each iteration, the weight matrix W receives a low rank change and we receive a new vector h. The goal is to maintain the projection matrix and answer the query B^⊤(BB^⊤)^{−1} Bh with good approximation guarantees. We design a fast dynamic data structure for this task and it is robust against an adaptive adversary. Following the beautiful and pioneering work of [Beimel, Kaplan, Mansour, Nissim, Saranurak and Stemmer, STOC’22], we use tools from differential privacy to reduce the randomness required by the data structure and further improve the running time.
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- Award ID(s):
- 2006359
- PAR ID:
- 10439438
- Date Published:
- Journal Name:
- Proceedings of the 40th International Conference on Machine Learning, PMLR
- Volume:
- 202
- Issue:
- 32418-32462
- ISSN:
- 2640-3498
- Page Range / eLocation ID:
- 1-45
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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