scholarly journals Understanding and Improving Proximity Graph Based Maximum Inner Product Search

2020 ◽  
Vol 34 (01) ◽  
pp. 139-146
Author(s):  
Jie Liu ◽  
Xiao Yan ◽  
Xinyan Dai ◽  
Zhirong Li ◽  
James Cheng ◽  
...  
Keyword(s):  
State Of The Art ◽  
Small World ◽  
Inner Product ◽  
Robust Performance ◽  
Small Norm ◽  
Order Of Magnitude ◽  
Product Search ◽  
Proximity Graph ◽  
Product Proximity ◽  
Strong Norm

The inner-product navigable small world graph (ip-NSW) represents the state-of-the-art method for approximate maximum inner product search (MIPS) and it can achieve an order of magnitude speedup over the fastest baseline. However, to date it is still unclear where its exceptional performance comes from. In this paper, we show that there is a strong norm bias in the MIPS problem, which means that the large norm items are very likely to become the result of MIPS. Then we explain the good performance of ip-NSW as matching the norm bias of the MIPS problem — large norm items have big in-degrees in the ip-NSW proximity graph and a walk on the graph spends the majority of computation on these items, thus effectively avoids unnecessary computation on small norm items. Furthermore, we propose the ip-NSW+ algorithm, which improves ip-NSW by introducing an additional angular proximity graph. Search is first conducted on the angular graph to find the angular neighbors of a query and then the MIPS neighbors of these angular neighbors are used to initialize the candidate pool for search on the inner-product proximity graph. Experiment results show that ip-NSW+ consistently and significantly outperforms ip-NSW and provides more robust performance under different data distributions.

scholarly journals A Bandit Approach to Maximum Inner Product Search

2019 ◽  
Vol 33 ◽  
pp. 4376-4383
Author(s):  
Rui Liu ◽  
Tianyi Wu ◽  
Barzan Mozafari
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Linear Time ◽  
Identification Problem ◽  
Approximate Algorithm ◽  
Inner Product ◽  
Approximate Algorithms ◽  
Product Search ◽  
Real World Datasets ◽  
The Cost

There has been substantial research on sub-linear time approximate algorithms for Maximum Inner Product Search (MIPS). To achieve fast query time, state-of-the-art techniques require significant preprocessing, which can be a burden when the number of subsequent queries is not sufficiently large to amortize the cost. Furthermore, existing methods do not have the ability to directly control the suboptimality of their approximate results with theoretical guarantees. In this paper, we propose the first approximate algorithm for MIPS that does not require any preprocessing, and allows users to control and bound the suboptimality of the results. We cast MIPS as a Best Arm Identification problem, and introduce a new bandit setting that can fully exploit the special structure of MIPS. Our approach outperforms state-of-the-art methods on both synthetic and real-world datasets.

scholarly journals Small footprint optoelectrodes using ring resonators for passive light localization

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Vittorino Lanzio ◽  
Gregory Telian ◽  
Alexander Koshelev ◽  
Paolo Micheletti ◽  
Gianni Presti ◽  
...  
Keyword(s):  
State Of The Art ◽  
Three Dimensional ◽  
Network Activity ◽  
Ring Resonators ◽  
Spatiotemporal Resolution ◽  
Optical Ring Resonators ◽  
Light Localization ◽  
Order Of Magnitude ◽  
Small Footprint

AbstractThe combination of electrophysiology and optogenetics enables the exploration of how the brain operates down to a single neuron and its network activity. Neural probes are in vivo invasive devices that integrate sensors and stimulation sites to record and manipulate neuronal activity with high spatiotemporal resolution. State-of-the-art probes are limited by tradeoffs involving their lateral dimension, number of sensors, and ability to access independent stimulation sites. Here, we realize a highly scalable probe that features three-dimensional integration of small-footprint arrays of sensors and nanophotonic circuits to scale the density of sensors per cross-section by one order of magnitude with respect to state-of-the-art devices. For the first time, we overcome the spatial limit of the nanophotonic circuit by coupling only one waveguide to numerous optical ring resonators as passive nanophotonic switches. With this strategy, we achieve accurate on-demand light localization while avoiding spatially demanding bundles of waveguides and demonstrate the feasibility with a proof-of-concept device and its scalability towards high-resolution and low-damage neural optoelectrodes.

Two Orders of Magnitude Improvement in the Detection Limit of Droplet-Based Micro-Magnetofluidics with Planar Hall Effect Sensors

2021 ◽  
Vol 6 (1) ◽  
pp. 47
Author(s):  
Julian Schütt ◽  
Rico Illing ◽  
Oleksii Volkov ◽  
Tobias Kosub ◽  
Pablo Nicolás Granell ◽  
...  
Keyword(s):  
Detection Limit ◽  
Hall Effect ◽  
State Of The Art ◽  
Limit Of Detection ◽  
Research Field ◽  
High Throughput Analysis ◽  
Planar Hall Effect ◽  
Biologically Relevant ◽  
Sensing Platform ◽  
Order Of Magnitude

The detection, manipulation, and tracking of magnetic nanoparticles is of major importance in the fields of biology, biotechnology, and biomedical applications as labels as well as in drug delivery, (bio-)detection, and tissue engineering. In this regard, the trend goes towards improvements of existing state-of-the-art methodologies in the spirit of timesaving, high-throughput analysis at ultra-low volumes. Here, microfluidics offers vast advantages to address these requirements, as it deals with the control and manipulation of liquids in confined microchannels. This conjunction of microfluidics and magnetism, namely micro-magnetofluidics, is a dynamic research field, which requires novel sensor solutions to boost the detection limit of tiny quantities of magnetized objects. We present a sensing strategy relying on planar Hall effect (PHE) sensors in droplet-based micro-magnetofluidics for the detection of a multiphase liquid flow, i.e., superparamagnetic aqueous droplets in an oil carrier phase. The high resolution of the sensor allows the detection of nanoliter-sized superparamagnetic droplets with a concentration of 0.58 mg cm−3, even when they are only biased in a geomagnetic field. The limit of detection can be boosted another order of magnitude, reaching 0.04 mg cm−³ (1.4 million particles in a single 100 nL droplet) when a magnetic field of 5 mT is applied to bias the droplets. With this performance, our sensing platform outperforms the state-of-the-art solutions in droplet-based micro-magnetofluidics by a factor of 100. This allows us to detect ferrofluid droplets in clinically and biologically relevant concentrations, and even in lower concentrations, without the need of externally applied magnetic fields.

Learning Sparse Binary Code for Maximum Inner Product Search

2021 ◽  
Author(s):  
Changyi Ma ◽  
Fangchen Yu ◽  
Yueyao Yu ◽  
Wenye Li
Keyword(s):  
Binary Code ◽  
Inner Product ◽  
Product Search

scholarly journals A Boosting Framework of Factorization Machine

2021 ◽  
pp. 2159036
Author(s):  
Jun Zhou ◽  
Longfei Li ◽  
Ziqi Liu ◽  
Chaochao Chen
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Experimental Results ◽  
Low Rank ◽  
Inner Product ◽  
Adaptive Boosting ◽  
Rank Matrix ◽  
Factorization Machine ◽  
Fixed Rank ◽  
Low Rank Matrix

Recently, Factorization Machine (FM) has become more and more popular for recommendation systems due to its effectiveness in finding informative interactions between features. Usually, the weights for the interactions are learned as a low rank weight matrix, which is formulated as an inner product of two low rank matrices. This low rank matrix can help improve the generalization ability of Factorization Machine. However, to choose the rank properly, it usually needs to run the algorithm for many times using different ranks, which clearly is inefficient for some large-scale datasets. To alleviate this issue, we propose an Adaptive Boosting framework of Factorization Machine (AdaFM), which can adaptively search for proper ranks for different datasets without re-training. Instead of using a fixed rank for FM, the proposed algorithm will gradually increase its rank according to its performance until the performance does not grow. Extensive experiments are conducted to validate the proposed method on multiple large-scale datasets. The experimental results demonstrate that the proposed method can be more effective than the state-of-the-art Factorization Machines.

New Instrumentation in Argonne's Hvem-Tandem Facility: Expanded Capability for in Situ Ion Beam Studies+

1995 ◽  
Vol 396 ◽  
Author(s):  
Charles W. Allen ◽  
Loren L. Funk ◽  
Edward A. Ryan
Keyword(s):  
Scientific Community ◽  
State Of The Art ◽  
Ion Irradiation ◽  
Ion Beam ◽  
Voltage Electron ◽  
User Facility ◽  
Order Of Magnitude ◽  
New Instrumentation ◽  
Better Than

AbstractDuring 1995, a state-of-the-art intermediate voltage electron microscope (IVEM) has been installed in the HVEM-Tandem Facility with in situ ion irradiation capabilities similar to those of the HVEM. A 300 kV Hitachi H-9000NAR has been interfaced to the two ion accelerators of the Facility, with a spatial resolution for imaging which is nearly an order of magnitude better than that for the 1.2 MV HVEM which dates from the early 1970s. The HVEM remains heavily utilized for electron- and ion irradiation-related materials studies, nevertheless, especially those for which less demanding microscopy is adequate. The capabilities and limitations of this IVEM and HVEM are compared. Both the HVEM and IVEM are part of the DOE funded User Facility and therefore are available to the scientific community for materials studies, free of charge for non-proprietary research.

Approximate Top-k Inner Product Join with a Proximity Graph

2021 ◽  
Author(s):  
Hayato Nakama ◽  
Daichi Amagata ◽  
Takahiro Hara
Keyword(s):  
Inner Product ◽  
Proximity Graph
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