Approximate K nearest neighbor (AKNN) search in the high-dimensional Euclidean vector space is a fundamental and challenging problem. We observe that space, time consumption of nearly all AKNN algorithms dominated by distance comparison operations (DCOs). For each operation, it scans full dimensions an object thus, runs linear wrt dimensionality. To speed up, we propose randomized algorithm named ADSampling which logarithmic dimensionality for majority DCOs succeeds with high probability. In...

Searching for approximate nearest neighbors (ANN) in the high-dimensional Euclidean space is a pivotal problem. Recently, with help of fast SIMD-based implementations, Product Quantization (PQ) and its variants can often efficiently accurately estimate distances between vectors have achieved great success in-memory ANN search. Despite their empirical success, we note that these methods do not theoretical error bound are observed to fail disastrously on some real-world datasets. Motivated by...

Abstract Constructing heterostructured photocatalysts and depositing an appropriate co‐catalyst to facilitate charge separation are crucial steps improve photocatalytic H 2 evolution from water splitting. Herein, we reported the synthesis of C‐doped ZrO /g‐C 3 N 4 /Ni P (C‐ZrO P) composite based on UiO‐66‐NH material for production under visible‐light irradiation. The optimal rate over C‐ZrO /20 %Ni was 10.04 mmol g −1 h , which more than 10 times higher that (0.90 ). apparent quantum yield...

Bimodal data, such as image-text pairs, has become increasingly prevalent in the digital era. The Hybrid Vector Query (HVQ) is an effective approach for querying data and recently garnered considerable attention from researchers. It calculates similarity scores objects represented by two vectors using a weighted sum of each individual vector's similarity, with query-specific parameter α to determine weight. Existing methods HVQ typically construct Approximate Nearest Neighbors Search (ANNS)...

Approximate nearest neighbor (ANN) search in high-dimensional Euclidean space has a broad range of applications. Among existing ANN algorithms, graph-based methods have shown superior performance terms the time-accuracy trade-off. However, they face bottlenecks due to random memory accesses caused by searching process on graph indices and costs computing exact distances guide process. To relieve bottlenecks, recent method named NGT-QG makes an attempt integrating quantization graph. It (1)...

Bimodal data, such as image-text pairs, has become increasingly prevalent in the digital era. The Hybrid Vector Query (HVQ) is an effective approach for querying data and recently garnered considerable attention from researchers. It calculates similarity scores objects represented by two vectors using a weighted sum of each individual vector's similarity, with query-specific parameter $\alpha$ to determine weight. Existing methods HVQ typically construct Approximate Nearest Neighbors Search...

<title>Abstract</title> The warm-mix recycled asphalt mixture (abbreviated as RAM), which integrates technique with recycling technology, offers significant energy savings and promotes the effective use of waste materials, delivering both environmental economic benefits. However, there has been limited research exploring water stability safety mechanisms SBS-modified SBSMA) mixture. For this reason, study selected Evotherm surface-active additive to investigate its impact on properties...

Gallibacterium anatis is a Gram-negative opportunistic avian pathogen representing an emerging threat to poultry meat and egg production worldwide. To date, no vaccine able effectively prevent the morbidity associated with G. infections has been developed yet. Our group previously reported that inoculation of different combinations outer membrane vesicles (OMVs), FlfA GtxA-N proteins effective in preventing lesions caused by layer chickens. Here we report testing efficacy as prototypes OMVs...

Most evaluation of the consistency multisensor images have focused on Normalized Difference Vegetation Index (NDVI) products for natural landscapes, often neglecting less vegetated urban landscapes. This gap has been filled through quantifying and evaluating spatial heterogeneity landscapes from QuickBird, Satellite pour l'observation de la Terre (SPOT), Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) Landsat Thematic Mapper (TM) with variogram analysis. Instead a...

Searching for approximate nearest neighbors (ANN) in the high-dimensional Euclidean space is a pivotal problem. Recently, with help of fast SIMD-based implementations, Product Quantization (PQ) and its variants can often efficiently accurately estimate distances between vectors have achieved great success in-memory ANN search. Despite their empirical success, we note that these methods do not theoretical error bound are observed to fail disastrously on some real-world datasets. Motivated by...

Abstract Seawater electrolysis is the most promising technology for hydrogen production, in which surface reconstruction on interface of electrode/electrolyte plays a crucial role activating catalytic reactions with low activation energy barrier. Herein, an efficient Mo modifying NiCoMo prickly flower clusters electrocatalyst supported nickel foam (Mo‐doped Ni/Co‐OOH clusters) obtained, serves as eminently active and durable catalyst both evolution reaction (HER) oxygen (OER) due to during...

Range-filtering approximate nearest neighbor (RFANN) search is attracting increasing attention in academia and industry. Given a set of data objects, each being pair high-dimensional vector numeric value, an RFANN query with range as parameters returns the object whose value to vector. To process this query, recent study proposes build $O(n^2)$ dedicated graph-based indexes for all possible ranges enable efficient processing on database $n$ objects. As storing these prohibitively expensive,...

Approximate nearest neighbor (ANN) query in high-dimensional Euclidean space is a key operator database systems. For this query, quantization popular family of methods developed for compressing vectors and reducing memory consumption. Recently, method called RaBitQ achieves the state-of-the-art performance among these methods. It produces better empirical both accuracy efficiency when using same compression rate provides rigorous theoretical guarantees. However, only designed at high rates...

Approximate nearest neighbor (ANN) search in high-dimensional Euclidean space has a broad range of applications. Among existing ANN algorithms, graph-based methods have shown superior performance terms the time-accuracy trade-off. However, they face bottlenecks due to random memory accesses caused by searching process on graph indices and costs computing exact distances guide process. To relieve bottlenecks, recent method named NGT-QG makes an attempt integrating quantization graph. It (1)...

Range-filtering approximate nearest neighbor (RFANN) search is attracting increasing attention in academia and industry. Given a set of data objects, each being pair high-dimensional vector numeric value, an RFANN query with range as parameters returns the object whose value to vector. To process this query, recent study proposes build O(n 2 ) dedicated graph-based indexes for all possible ranges enable efficient processing on database n objects. As storing these prohibitively expensive,...

The boron-nitrogen-containing phenol-formaldehyde resin (BNPFR)/SiO2 nanocomposites (BNPFR/SiO2) were synthesized in-situ, and structure of BNPFR/SiO2 was characterized by FTIR, XRD TEM. loss modulus peak temperature Tp cured with different nano-SiO2 content are determined torsional braid analysis (TBA). thermal degradation kinetics investigated thermogravimetric (TGA). results show that particulate about 50 nm diameter has a more uniformly distribution in the samples. nanocomposite is 214...

Approximate K nearest neighbor (AKNN) search is a fundamental and challenging problem. We observe that in high-dimensional space, the time consumption of nearly all AKNN algorithms dominated by distance comparison operations (DCOs). For each operation, it scans full dimensions an object thus, runs linear wrt dimensionality. To speed up, we propose randomized algorithm named ADSampling which logarithmic to dimensionality for majority DCOs succeeds with high probability. In addition, based on...