154 research outputs found
Locality Preserving Projections for Grassmann manifold
Learning on Grassmann manifold has become popular in many computer vision
tasks, with the strong capability to extract discriminative information for
imagesets and videos. However, such learning algorithms particularly on
high-dimensional Grassmann manifold always involve with significantly high
computational cost, which seriously limits the applicability of learning on
Grassmann manifold in more wide areas. In this research, we propose an
unsupervised dimensionality reduction algorithm on Grassmann manifold based on
the Locality Preserving Projections (LPP) criterion. LPP is a commonly used
dimensionality reduction algorithm for vector-valued data, aiming to preserve
local structure of data in the dimension-reduced space. The strategy is to
construct a mapping from higher dimensional Grassmann manifold into the one in
a relative low-dimensional with more discriminative capability. The proposed
method can be optimized as a basic eigenvalue problem. The performance of our
proposed method is assessed on several classification and clustering tasks and
the experimental results show its clear advantages over other Grassmann based
algorithms.Comment: Accepted by IJCAI 201
Delving into Discrete Normalizing Flows on SO(3) Manifold for Probabilistic Rotation Modeling
Normalizing flows (NFs) provide a powerful tool to construct an expressive
distribution by a sequence of trackable transformations of a base distribution
and form a probabilistic model of underlying data. Rotation, as an important
quantity in computer vision, graphics, and robotics, can exhibit many
ambiguities when occlusion and symmetry occur and thus demands such
probabilistic models. Though much progress has been made for NFs in Euclidean
space, there are no effective normalizing flows without discontinuity or
many-to-one mapping tailored for SO(3) manifold. Given the unique non-Euclidean
properties of the rotation manifold, adapting the existing NFs to SO(3)
manifold is non-trivial. In this paper, we propose a novel normalizing flow on
SO(3) by combining a Mobius transformation-based coupling layer and a
quaternion affine transformation. With our proposed rotation normalizing flows,
one can not only effectively express arbitrary distributions on SO(3), but also
conditionally build the target distribution given input observations. Extensive
experiments show that our rotation normalizing flows significantly outperform
the baselines on both unconditional and conditional tasks.Comment: CVPR 202
Japan prefectural emission accounts and socioeconomic data 2007 to 2015
In the wake of the Fukushima nuclear disaster, Japan largely moved away from nuclear power generation and turned back towards an energy sector dominated by fossil fuels. As a result, the pace towards reaching emission reduction targets has largely slowed down. This situation indicates that higher emissions will continue to be generated if there is no appropriate and efficient measurement implemented to bridge the energy demand gap. To contribute adequate mitigation policies, a detailed inventory of both CO2 emissions and socioeconomic factors, both at the national and regional level, should be issued. Thereby, this work contributes to a time-series emission with a record of 47 prefectures in Japan as well as their associated socioeconomic features. The compiled emission inventory is based on three major fossil fuels and 26 sectors with careful emission allocations for regional electricity generation. This dataset is uniformly formatted and can be expected to provide vital information to set regional reduction allowances and sectoral reduction priorities
A Phase-Coded Time-Domain Interleaved OTFS Waveform with Improved Ambiguity Function
Integrated sensing and communication (ISAC) is a significant application
scenario in future wireless communication networks, and sensing capability of a
waveform is always evaluated by the ambiguity function. To enhance the sensing
performance of the orthogonal time frequency space (OTFS) waveform, we propose
a novel time-domain interleaved cyclic-shifted P4-coded OTFS (TICP4-OTFS) with
improved ambiguity function. TICP4-OTFS can achieve superior autocorrelation
features in both the time and frequency domains by exploiting the
multicarrier-like form of OTFS after interleaved and the favorable
autocorrelation attributes of the P4 code. Furthermore, we present the
vectorized formulation of TICP4-OTFS modulation as well as its signal structure
in each domain. Numerical simulations show that our proposed TICP4-OTFS
waveform outperforms OTFS with a narrower mainlobe as well as lower and more
distant sidelobes in terms of delay and Doppler-dimensional ambiguity
functions, and an instance of range estimation using pulse compression is
illustrated to exhibit the proposed waveform\u2019s greater resolution.
Besides, TICP4-OTFS achieves better performance of bit error rate for
communication in low signal-to-noise ratio (SNR) scenarios.Comment: This paper has been accepted by 2023 IEEE Globecom Workshops (GC
Wkshps): Workshop on Integrated Sensing and Communications for Internet of
Thing
Cyclic Delay-Doppler Shift: A Simple Transmit Diversity Technique for Delay-Doppler Waveforms in Doubly Selective Channels
Delay-Doppler waveform design has been considered as a promising solution to
achieve reliable communication under high-mobility channels for the
space-air-ground-integrated networks (SAGIN). In this paper, we introduce the
cyclic delay-Doppler shift (CDDS) technique for delay-Doppler waveforms to
extract transmit diversity in doubly selective channels. Two simple CDDS
schemes, named time-domain CDDS (TD-CDDS) and modulation-domain CDDS (MD-CDDS),
are proposed in the setting of multiple-input multiple-output (MIMO). We
demonstrate the applications of CDDS on two representative delay-Doppler
waveforms, namely orthogonal time frequency space (OTFS) and affine frequency
division multiplexing (AFDM), by deriving their corresponding CDDS matrices.
Furthermore, we prove theoretically and experimentally that CDDS can provide
OTFS and AFDM with full transmit diversity gain on most occasions
MIMO-DoAnet: Multi-channel Input and Multiple Outputs DoA Network with Unknown Number of Sound Sources
Recent neural network based Direction of Arrival (DoA) estimation algorithms
have performed well on unknown number of sound sources scenarios. These
algorithms are usually achieved by mapping the multi-channel audio input to the
single output (i.e. overall spatial pseudo-spectrum (SPS) of all sources), that
is called MISO. However, such MISO algorithms strongly depend on empirical
threshold setting and the angle assumption that the angles between the sound
sources are greater than a fixed angle. To address these limitations, we
propose a novel multi-channel input and multiple outputs DoA network called
MIMO-DoAnet. Unlike the general MISO algorithms, MIMO-DoAnet predicts the SPS
coding of each sound source with the help of the informative spatial covariance
matrix. By doing so, the threshold task of detecting the number of sound
sources becomes an easier task of detecting whether there is a sound source in
each output, and the serious interaction between sound sources disappears
during inference stage. Experimental results show that MIMO-DoAnet achieves
relative 18.6% and absolute 13.3%, relative 34.4% and absolute 20.2% F1 score
improvement compared with the MISO baseline system in 3, 4 sources scenes. The
results also demonstrate MIMO-DoAnet alleviates the threshold setting problem
and solves the angle assumption problem effectively.Comment: Accepted by Interspeech 202
Mouse hepatocyte overexpression of NFâÎșBâinducing kinase (NIK) triggers fatal macrophageâdependent liver injury and fibrosis
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109646/1/hep27348-sup-0001-suppinfo01.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/109646/2/hep27348.pd
Locate and Beamform: Two-dimensional Locating All-neural Beamformer for Multi-channel Speech Separation
Recently, stunning improvements on multi-channel speech separation have been
achieved by neural beamformers when direction information is available.
However, most of them neglect to utilize speaker's 2-dimensional (2D) location
cues contained in mixture signal, which limits the performance when two sources
come from close directions. In this paper, we propose an end-to-end beamforming
network for 2D location guided speech separation merely given mixture signal.
It first estimates discriminable direction and 2D location cues, which imply
directions the sources come from in multi views of microphones and their 2D
coordinates. These cues are then integrated into location-aware neural
beamformer, thus allowing accurate reconstruction of two sources' speech
signals. Experiments show that our proposed model not only achieves a
comprehensive decent improvement compared to baseline systems, but avoids
inferior performance on spatial overlapping cases.Comment: Accepted by Interspeech 2023. arXiv admin note: substantial text
overlap with arXiv:2212.0340
- âŠ