5,142 research outputs found
HOS-Miner: a system for detecting outlying subspaces of high-dimensional data
[Abstract]: We identify a new and interesting high-dimensional outlier detection problem in this paper that is, detecting the subspaces in which given data points are outliers. We call the subspaces in which a data point is an outlier as its Outlying Subspaces. In this paper, we will propose the prototype of a dynamic subspace search system, called HOS-Miner (HOS stands for High-dimensional Outlying Subspaces) that utilizes a sample-based learning process to effectively identify the outlying subspaces of a given point
Discrete Diffusion Language Modeling by Estimating the Ratios of the Data Distribution
Despite their groundbreaking performance for many generative modeling tasks,
diffusion models have fallen short on discrete data domains such as natural
language. Crucially, standard diffusion models rely on the well-established
theory of score matching, but efforts to generalize this to discrete structures
have not yielded the same empirical gains. In this work, we bridge this gap by
proposing score entropy, a novel discrete score matching loss that is more
stable than existing methods, forms an ELBO for maximum likelihood training,
and can be efficiently optimized with a denoising variant. We scale our Score
Entropy Discrete Diffusion models (SEDD) to the experimental setting of GPT-2,
achieving highly competitive likelihoods while also introducing distinct
algorithmic advantages. In particular, when comparing similarly sized SEDD and
GPT-2 models, SEDD attains comparable perplexities (normally within of
and sometimes outperforming the baseline). Furthermore, SEDD models learn a
more faithful sequence distribution (around better compared to GPT-2
models with ancestral sampling as measured by large models), can trade off
compute for generation quality (needing only fewer network
evaluations to match GPT-2), and enables arbitrary infilling beyond the
standard left to right prompting.Comment: 30 page
Dark matter dominated dwarf disc galaxy Segue 1
Several observations reveal that dwarf galaxy Segue 1 has a dark matter (DM)
halo at least ~ 200 times more massive than its visible baryon mass of only ~
103 solar masses. The baryon mass is dominated by stars with perhaps an
interstellar gas mass of < 13 solar masses. Regarding Segue 1 as a dwarf disc
galaxy by its morphological appearance of long stretch, we invoke the dynamic
model of Xiang-Gruess, Lou & Duschl (XLD) to estimate its physical parameters
for possible equilibria with and without an isopedically magnetized gas disc.
We estimate the range of DM mass and compare it with available observational
inferences. Due to the relatively high stellar velocity dispersion compared to
the stellar surface mass density, we find that a massive DM halo would be
necessary to sustain disc equilibria. The required DM halo mass agrees grossly
with observational inferences so far. For an isopedic magnetic field in a gas
disc, the ratio f between the DM and baryon potentials depends strongly on the
magnetic field strength. Therefore, a massive DM halo is needed to counteract
either the strong stellar velocity dispersion and rotation of the stellar disc
or the magnetic Lorentz force in the gas disc. By the radial force balances,
the DM halo mass increases for faster disc rotation.Comment: 5 pages, 2 figures, accepted for publication in MNRA
Modal Perturbation Method for the Dynamic Characteristics of Timoshenko Beams
Timoshenko beams have been widely used in structural and mechanical systems. under dynamic loading, the analytical solution of a Timoshenko beam is often difficult to obtain due to the complexity involved in the equation of motion. In this paper, a modal perturbation method is introduced to approximately determine the dynamic characteristics of a Timoshenko beam. In this approach, the differential equation of motion describing the dynamic behavior of the Timoshenko beam can be transformed into a set of nonlinear algebraic equations. Therefore, the solution process can be simplified significantly for the Timoshenko beam with arbitrary boundaries. Several examples are given to illustrate the application of the proposed method. Numerical results have shown that the modal perturbation method is effective in determining the modal characteristics of Timoshenko beams with high accuracy. The effects of shear distortion and moment of inertia on the natural frequencies of Timoshenko beams are discussed in detail
TGR5: A Novel Target for Weight Maintenance and Glucose Metabolism
TGR5, an emerging G protein-coupled receptor, was identified as a membrane receptor for bile acids. The expression of TGR5 and its function are distinct from the previously identified nuclear bile acid receptor, farnesoid X receptor (FXR). These two bile acid receptors complement with each other for maintaining bile acid homeostasis and mediating bile acid signaling. Both receptors are also shown to play roles in regulating inflammation and glucose metabolism. An interesting finding for TGR5 is its role in energy metabolism. The discovery of TGR5 expression in brown adipocyte tissues (BATs) and the recent demonstration of BAT in adult human body suggest a potential approach to combat obesity by targeting TGR5 to increase thermogenesis. We summarize here the latest finding of TGR5 research, especially its role in energy metabolism and glucose homeostasis
TransXNet: Learning Both Global and Local Dynamics with a Dual Dynamic Token Mixer for Visual Recognition
Recent studies have integrated convolution into transformers to introduce
inductive bias and improve generalization performance. However, the static
nature of conventional convolution prevents it from dynamically adapting to
input variations, resulting in a representation discrepancy between convolution
and self-attention as self-attention calculates attention matrices dynamically.
Furthermore, when stacking token mixers that consist of convolution and
self-attention to form a deep network, the static nature of convolution hinders
the fusion of features previously generated by self-attention into convolution
kernels. These two limitations result in a sub-optimal representation capacity
of the constructed networks. To find a solution, we propose a lightweight Dual
Dynamic Token Mixer (D-Mixer) that aggregates global information and local
details in an input-dependent way. D-Mixer works by applying an efficient
global attention module and an input-dependent depthwise convolution separately
on evenly split feature segments, endowing the network with strong inductive
bias and an enlarged effective receptive field. We use D-Mixer as the basic
building block to design TransXNet, a novel hybrid CNN-Transformer vision
backbone network that delivers compelling performance. In the ImageNet-1K image
classification task, TransXNet-T surpasses Swin-T by 0.3% in top-1 accuracy
while requiring less than half of the computational cost. Furthermore,
TransXNet-S and TransXNet-B exhibit excellent model scalability, achieving
top-1 accuracy of 83.8% and 84.6% respectively, with reasonable computational
costs. Additionally, our proposed network architecture demonstrates strong
generalization capabilities in various dense prediction tasks, outperforming
other state-of-the-art networks while having lower computational costs. Code is
available at https://github.com/LMMMEng/TransXNet.Comment: 12 pages, 8 figures. This work has been submitted to the IEEE for
possible publication. Copyright may be transferred without notice, after
which this version may no longer be accessibl
SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imaging
Automated classification of liver lesions in multi-phase CT and MR scans is
of clinical significance but challenging. This study proposes a novel Siamese
Dual-Resolution Transformer (SDR-Former) framework, specifically designed for
liver lesion classification in 3D multi-phase CT and MR imaging with varying
phase counts. The proposed SDR-Former utilizes a streamlined Siamese Neural
Network (SNN) to process multi-phase imaging inputs, possessing robust feature
representations while maintaining computational efficiency. The weight-sharing
feature of the SNN is further enriched by a hybrid Dual-Resolution Transformer
(DR-Former), comprising a 3D Convolutional Neural Network (CNN) and a tailored
3D Transformer for processing high- and low-resolution images, respectively.
This hybrid sub-architecture excels in capturing detailed local features and
understanding global contextual information, thereby, boosting the SNN's
feature extraction capabilities. Additionally, a novel Adaptive Phase Selection
Module (APSM) is introduced, promoting phase-specific intercommunication and
dynamically adjusting each phase's influence on the diagnostic outcome. The
proposed SDR-Former framework has been validated through comprehensive
experiments on two clinical datasets: a three-phase CT dataset and an
eight-phase MR dataset. The experimental results affirm the efficacy of the
proposed framework. To support the scientific community, we are releasing our
extensive multi-phase MR dataset for liver lesion analysis to the public. This
pioneering dataset, being the first publicly available multi-phase MR dataset
in this field, also underpins the MICCAI LLD-MMRI Challenge. The dataset is
accessible at:https://bit.ly/3IyYlgN.Comment: 13 pages, 7 figure
Exogenous Application of Jasmonic Acid Induces Volatile Emissions in Rice and Enhances Parasitism of Nilaparvata lugens Eggs by theParasitoid Anagrus nilaparvatae
Jasmonate signaling pathway plays an important role in induced plant defense against herbivores and pathogens, including the emission of volatiles that serve as attractants for natural enemies of herbivores. We studied the volatiles emitted from rice plants that were wounded and treated with jasmonic acid (JA) and their effects on the host-searching behavior of the rice brown planthopper, Nilaparvata lugens (Stål), and its mymarid egg parasitoid Anagrus nilaparvatae Pang et Wang. Female adults of N. lugens significantly preferred to settle on JA-treated rice plants immediately after release. The parasitoid A. nilaparvatae showed a similar preference and was more attracted to the volatiles emitted from JA-treated rice plants than to volatiles from control plants. This was also evident from greenhouse and field experiments in which parasitism of N. lugens eggs by A. nilaparvatae on plants that were surrounded by JA-treated plants was more than twofold higher than on control plants. Analyses of volatiles collected from rice plants showed that JA treatment dramatically increased the release of volatiles, which included aliphatic aldehydes and alcohols, monoterpenes, sesquiterpenes, methyl salicylate, n-heptadecane, and several as yet unidentified compounds. These results confirm an involvement of the JA pathway in induced defense in rice plants and demonstrate that the egg parasitoid A. nilaparvatae exploits plant-provided cues to locate hosts. We explain the use of induced plant volatiles by the egg parasitoid by a reliable association between planthopper feeding damage and egg presenc
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