59 research outputs found
Convolutional Embedding for Edit Distance
Edit-distance-based string similarity search has many applications such as
spell correction, data de-duplication, and sequence alignment. However,
computing edit distance is known to have high complexity, which makes string
similarity search challenging for large datasets. In this paper, we propose a
deep learning pipeline (called CNN-ED) that embeds edit distance into Euclidean
distance for fast approximate similarity search. A convolutional neural network
(CNN) is used to generate fixed-length vector embeddings for a dataset of
strings and the loss function is a combination of the triplet loss and the
approximation error. To justify our choice of using CNN instead of other
structures (e.g., RNN) as the model, theoretical analysis is conducted to show
that some basic operations in our CNN model preserve edit distance.
Experimental results show that CNN-ED outperforms data-independent CGK
embedding and RNN-based GRU embedding in terms of both accuracy and efficiency
by a large margin. We also show that string similarity search can be
significantly accelerated using CNN-based embeddings, sometimes by orders of
magnitude.Comment: Accepted by the 43rd International ACM SIGIR Conference on Research
and Development in Information Retrieval, 202
Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search
Vector quantization (VQ) techniques are widely used in similarity search for
data compression, fast metric computation and etc. Originally designed for
Euclidean distance, existing VQ techniques (e.g., PQ, AQ) explicitly or
implicitly minimize the quantization error. In this paper, we present a new
angle to analyze the quantization error, which decomposes the quantization
error into norm error and direction error. We show that quantization errors in
norm have much higher influence on inner products than quantization errors in
direction, and small quantization error does not necessarily lead to good
performance in maximum inner product search (MIPS). Based on this observation,
we propose norm-explicit quantization (NEQ) --- a general paradigm that
improves existing VQ techniques for MIPS. NEQ quantizes the norms of items in a
dataset explicitly to reduce errors in norm, which is crucial for MIPS. For the
direction vectors, NEQ can simply reuse an existing VQ technique to quantize
them without modification. We conducted extensive experiments on a variety of
datasets and parameter configurations. The experimental results show that NEQ
improves the performance of various VQ techniques for MIPS, including PQ, OPQ,
RQ and AQ
Understanding and Improving Proximity Graph based Maximum Inner Product Search
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.Comment: 8 pages, 8 figure
InstructME: An Instruction Guided Music Edit And Remix Framework with Latent Diffusion Models
Music editing primarily entails the modification of instrument tracks or
remixing in the whole, which offers a novel reinterpretation of the original
piece through a series of operations. These music processing methods hold
immense potential across various applications but demand substantial expertise.
Prior methodologies, although effective for image and audio modifications,
falter when directly applied to music. This is attributed to music's
distinctive data nature, where such methods can inadvertently compromise the
intrinsic harmony and coherence of music. In this paper, we develop InstructME,
an Instruction guided Music Editing and remixing framework based on latent
diffusion models. Our framework fortifies the U-Net with multi-scale
aggregation in order to maintain consistency before and after editing. In
addition, we introduce chord progression matrix as condition information and
incorporate it in the semantic space to improve melodic harmony while editing.
For accommodating extended musical pieces, InstructME employs a chunk
transformer, enabling it to discern long-term temporal dependencies within
music sequences. We tested InstructME in instrument-editing, remixing, and
multi-round editing. Both subjective and objective evaluations indicate that
our proposed method significantly surpasses preceding systems in music quality,
text relevance and harmony. Demo samples are available at
https://musicedit.github.io/Comment: Demo samples are available at https://musicedit.github.io
Geometric Symmetry of Dielectric Antenna Influencing Light Absorption in Quantum-Sized Metal Nanocrystals: A Comparative Study
Silica nanoparticles, optically transparent in the visible spectral region, represent a class of dielectric antenna to tune the propagation and local field distribution of the visible light through surface scattering while the energy loss is minimized. The light scattering on the surface of silica nanoparticles include resonant scattering and random scattering that strongly depend on their geometry: spherical silica nanoparticles with the highest geometrical symmetry favors the light scattering resonances on the nanoparticle surfaces to promote resonant scattering while non-spherical silica nanoparticles mainly support random scattering. Both resonant scattering and random scattering of light on the silica nanoparticles are capable of enhancing the light absorption in quantum-sized metal nanocrystals attached to the surfaces of the silica nanoparticles. The contributions of resonant scattering and random scattering to the enhancement of light absorption have been compared and discussed. The understanding highlights the importance of the geometry of the silica nanoparticle antenna on the design and synthesis of composite materials for efficient light harvesting
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