The irreversible dimerization of the acetophenone radical anion, chosen as an example of a carbon-carbon coupling reaction between two charged species, was investigated in a series of 1,3-dialkylimidazolium and 1,2,3-trialkylimidazolium ionic liquids. Indeed, such ion dimerizations which display slow kinetics despite small activation energies, are controlled by a subtle competition between bond formation, Coulombic repulsion and solvation. The effects of viscosity, "polarity" and ionic solvation on the reactivity of the radical anions were examined. The dimerization rate constants were demonstrated to be only weakly affected by the high viscosity of the medium or its apparent polarity. When the acetophenone radical anion is "solvated" in imidazolium-based ionic liquids, a strong interaction between the negatively-charged intermediates and the imidazolium cation occurs. The ensuing charge stabilization allows a fast dimerization step in all the ionic liquids used. The kinetic effect is even enhanced in the 1,3-dialkylimidazolium salts as compared to the 1,2,3-trialkylimidazolium ones because the interaction between the radical anions and the 1,3-dialkylimidazolium cations are stronger, probably due to the formation of H-bond. The reactivity of the ion radical is demonstrated not only to be mainly dominated by electrostatic interactions, but also that the nature of the ionic liquid cations with respect to that of the ion radical is a major factor that affects the reaction kinetics.

E cient Nearest Neighbor (NN) search in high-dimensional spaces is a foundation of many multimedia retrieval systems. Because it o ers low responses times, Product Quantization (PQ) is a popular solution. PQ compresses high-dimensional vectors into short codes using several sub-quantizers, which enables in-RAM storage of large databases. This allows fast answers to NN queries, without accessing the SSD or HDD. The key feature of PQ is that it can compute distances between short codes and high-dimensional vectors using cache-resident lookup tables. The e ciency of this technique, named Asymmetric Distance Computation (ADC), remains limited because it performs many cache accesses.In this paper, we introduce Quick ADC, a novel technique that achieves a 3 to 6 times speedup over ADC by exploiting Single Instruction Multiple Data (SIMD) units available in current CPUs. Efciently exploiting SIMD requires algorithmic changes to the ADC procedure. Namely, Quick ADC relies on two key modi cations of ADC: (i) the use 4-bit sub-quantizers instead of the standard 8-bit sub-quantizers and (ii) the quantization of oating-point distances. This allows Quick ADC to exceed the performance of state-of-theart systems, e.g., it achieves a Recall@100 of 0.94 in 3.4 ms on 1 billion SIFT descriptors (128-bit codes).

Efficient Nearest Neighbor (NN) search in high-dimensional spaces is a foundation of many multimedia retrieval systems. A common approach is to rely on Product Quantization, which allows the storage of large vector databases in memory and efficient distance computations. Yet, implementations of nearest neighbor search with Product Quantization have their performance limited by the many memory accesses they perform. Following this observation, André et al. proposed Quick ADC with up to 6× faster implementations of PQ m×4 product quantizers (PQ) leveraging specific SIMD instructions. Quicker ADC is a generalization of Quick ADC not limited to PQ m×4 codes and supporting AVX-512, the latest revision of SIMD instruction set. In doing so, Quicker ADC faces the challenge of using efficiently 5,6 and 7-bit shuffles that do not align to computer bytes or words. To this end, we introduce (i) irregular product quantizers combining sub-quantizers of different granularity and (ii) split tables allowing lookup tables larger than registers. We evaluate Quicker ADC with multiple indexes including Inverted Multi-Indexes and IVF HNSW and show that it outperforms the reference optimized implementations (i.e., FAISS and polysemous codes) for numerous configurations. Finally, we release an open-source fork of FAISS enhanced with Quicker ADC.