_id,doi,title
10803,10.48550/arXiv.2102.05996,Fairness through regularization for learning to rank
10806,10.1021/jacsau.1c00349,Ligand conversion in nanocrystal synthesis: The oxidation of alkylamines to fatty acids by nitrate
10809,10.1126/science.abg0886,Tidying up the mess
10816,10.1038/s43588-021-00157-1,"How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network"
10834,10.1016/j.cub.2021.02.043,"Loss of Hem1 disrupts macrophage function and impacts migration, phagocytosis, and integrin-mediated adhesion"
10836,10.1111/all.14604,"PIPE‐cloned human IgE and IgG4 antibodies: New tools for investigating cow's milk allergy and tolerance"
10838,10.1111/mec.15861,Using replicate hybrid zones to understand the genomic basis of adaptive divergence
10847,10.24963/ijcai.2021/575,Solving partially observable stochastic shortest-path games
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