_id,doi,title
10852,10.1142/s0129055x20600120,The polaron at strong coupling
10853,10.1145/3409964.3461810,A scalable concurrent algorithm for dynamic connectivity
10854,10.1145/3410220.3453923,Input-dynamic distributed algorithms for communication networks
10855,10.1145/3447384,Input-dynamic distributed algorithms for communication networks
10856,10.1515/agms-2020-0103,On the volume of sections of the cube
10858,10.3390/nano11071827,Enhanced thermoelectric performance of n-type Bi2Se3 nanosheets through Sn doping
10860,10.4153/s000843952000096x,Tight frames and related geometric problems
10912,10.48550/ARXIV.2108.06686,Quantifying the coexistence of neuronal oscillations and avalanches
11436,,Asynchronous optimization methods for efficient training of deep neural networks with guarantees
11452,,Distributed principal component analysis with limited communication
11453,,Online learning of neural computations from sparse temporal feedback
11458,,AC/DC: Alternating Compressed/DeCompressed training of deep neural networks
11463,,M-FAC: Efficient matrix-free approximations of second-order information
11464,,Towards tight communication lower bounds for distributed optimisation
7883,10.1002/wdev.383,Regulation of size and scale in vertebrate spinal cord development
7900,10.1142/s0129055x20600090,Bosonic collective excitations in Fermi gases
7901,10.1007/s00222-021-01041-5,Correlation energy of a weakly interacting Fermi gas
7905,10.1007/s00454-020-00206-y,Sheaf-theoretic stratification learning from geometric and topological perspectives
7925,10.1007/s11590-020-01603-1,New inertial relaxed method for solving split feasibilities
7939,10.1007/s00446-020-00380-5,Fast approximate shortest paths in the congested clique