159 research outputs found
LightXML: Transformer with Dynamic Negative Sampling for High-Performance Extreme Multi-label Text Classification
Extreme Multi-label text Classification (XMC) is a task of finding the most
relevant labels from a large label set. Nowadays deep learning-based methods
have shown significant success in XMC. However, the existing methods (e.g.,
AttentionXML and X-Transformer etc) still suffer from 1) combining several
models to train and predict for one dataset, and 2) sampling negative labels
statically during the process of training label ranking model, which reduces
both the efficiency and accuracy of the model. To address the above problems,
we proposed LightXML, which adopts end-to-end training and dynamic negative
labels sampling. In LightXML, we use generative cooperative networks to recall
and rank labels, in which label recalling part generates negative and positive
labels, and label ranking part distinguishes positive labels from these labels.
Through these networks, negative labels are sampled dynamically during label
ranking part training by feeding with the same text representation. Extensive
experiments show that LightXML outperforms state-of-the-art methods in five
extreme multi-label datasets with much smaller model size and lower
computational complexity. In particular, on the Amazon dataset with 670K
labels, LightXML can reduce the model size up to 72% compared to AttentionXML
Perception of Misalignment States for Sky Survey Telescopes with the Digital Twin and the Deep Neural Networks
Sky survey telescopes play a critical role in modern astronomy, but
misalignment of their optical elements can introduce significant variations in
point spread functions, leading to reduced data quality. To address this, we
need a method to obtain misalignment states, aiding in the reconstruction of
accurate point spread functions for data processing methods or facilitating
adjustments of optical components for improved image quality. Since sky survey
telescopes consist of many optical elements, they result in a vast array of
potential misalignment states, some of which are intricately coupled, posing
detection challenges. However, by continuously adjusting the misalignment
states of optical elements, we can disentangle coupled states. Based on this
principle, we propose a deep neural network to extract misalignment states from
continuously varying point spread functions in different field of views. To
ensure sufficient and diverse training data, we recommend employing a digital
twin to obtain data for neural network training. Additionally, we introduce the
state graph to store misalignment data and explore complex relationships
between misalignment states and corresponding point spread functions, guiding
the generation of training data from experiments. Once trained, the neural
network estimates misalignment states from observation data, regardless of the
impacts caused by atmospheric turbulence, noise, and limited spatial sampling
rates in the detector. The method proposed in this paper could be used to
provide prior information for the active optics system and the optical system
alignment.Comment: The aforementioned submission has been accepted by Optics Express. We
kindly request any feedback or comments to be directed to the corresponding
author, Peng Jia ([email protected]), or the second corresponding
author, Zhengyang Li ([email protected]). Please note that Zhengyang is
currently stationed in the South Antarctica and will not be available until
after February 1st, 202
A Survey of Graph Pre-processing Methods: From Algorithmic to Hardware Perspectives
Graph-related applications have experienced significant growth in academia
and industry, driven by the powerful representation capabilities of graph.
However, efficiently executing these applications faces various challenges,
such as load imbalance, random memory access, etc. To address these challenges,
researchers have proposed various acceleration systems, including software
frameworks and hardware accelerators, all of which incorporate graph
pre-processing (GPP). GPP serves as a preparatory step before the formal
execution of applications, involving techniques such as sampling, reorder, etc.
However, GPP execution often remains overlooked, as the primary focus is
directed towards enhancing graph applications themselves. This oversight is
concerning, especially considering the explosive growth of real-world graph
data, where GPP becomes essential and even dominates system running overhead.
Furthermore, GPP methods exhibit significant variations across devices and
applications due to high customization. Unfortunately, no comprehensive work
systematically summarizes GPP. To address this gap and foster a better
understanding of GPP, we present a comprehensive survey dedicated to this area.
We propose a double-level taxonomy of GPP, considering both algorithmic and
hardware perspectives. Through listing relavent works, we illustrate our
taxonomy and conduct a thorough analysis and summary of diverse GPP techniques.
Lastly, we discuss challenges in GPP and potential future directions
A fast tunable driver of light source for the TRIDENT Pathfinder experiment
TRIDENT (The tRopIcal DEep-sea Neutrino Telescope) is a proposed
next-generation neutrino telescope to be constructed in the South China Sea. In
September 2021, the TRIDENT Pathfinder experiment (TRIDENT EXplorer, T-REX for
short) was conducted to evaluate the in-situ optical properties of seawater.
The T-REX experiment deployed three digital optical modules at a depth of 3420
meters, including a light emitter module (LEM) and two light receiver modules
(LRMs) equipped with photomultiplier tubes (PMTs) and cameras to detect light
signals. The LEM emits light in pulsing and steady modes. It features a fast
tunable driver to activate light-emitting diodes (LEDs) that emit
nanosecond-width light pulses with tunable intensity. The PMTs in the LRM
receive single photo-electron (SPE) signals with an average photon number of
approximately 0.3 per 1-microsecond time window, which is used to measure the
arrival time distribution of the SPE signals. The fast tunable driver can be
remotely controlled in real-time by the data acquisition system onboard the
research vessel, allowing for convenient adjustments to the driver's parameters
and facilitating the acquisition of high-quality experimental data. This paper
describes the requirements, design scheme, and test results of the fast tunable
driver, highlighting its successful implementation in the T-REX experiment and
its potential for future deep-sea experiments
Generative Model for Models: Rapid DNN Customization for Diverse Tasks and Resource Constraints
Unlike cloud-based deep learning models that are often large and uniform,
edge-deployed models usually demand customization for domain-specific tasks and
resource-limited environments. Such customization processes can be costly and
time-consuming due to the diversity of edge scenarios and the training load for
each scenario. Although various approaches have been proposed for rapid
resource-oriented customization and task-oriented customization respectively,
achieving both of them at the same time is challenging. Drawing inspiration
from the generative AI and the modular composability of neural networks, we
introduce NN-Factory, an one-for-all framework to generate customized
lightweight models for diverse edge scenarios. The key idea is to use a
generative model to directly produce the customized models, instead of training
them. The main components of NN-Factory include a modular supernet with
pretrained modules that can be conditionally activated to accomplish different
tasks and a generative module assembler that manipulate the modules according
to task and sparsity requirements. Given an edge scenario, NN-Factory can
efficiently customize a compact model specialized in the edge task while
satisfying the edge resource constraints by searching for the optimal strategy
to assemble the modules. Based on experiments on image classification and
object detection tasks with different edge devices, NN-Factory is able to
generate high-quality task- and resource-specific models within few seconds,
faster than conventional model customization approaches by orders of magnitude
Computer-Aided Drug Design of Capuramycin Analogues as Anti-Tuberculosis Antibiotics by 3D-QSAR and Molecular Docking
Capuramycin and a few semisynthetic derivatives have shown potential as anti-tuberculosis antibiotics.To understand their mechanism of action and structureactivity relationships a 3D-QSAR and molecular docking studies were performed. A set of 52 capuramycin derivatives for the training set and 13 for the validation set was used. A highly predictive MFA model was obtained with crossvalidated q2 of 0.398, and non-cross validated partial least-squares (PLS) analysis showed a conventional r2 of 0.976 and r2pred of 0.839. The model has an excellent predictive ability. Combining the 3D-QSAR and molecular docking studies, a number of new capuramycin analogs with predicted improved activities were designed. Biological activity tests of one analog showed useful antibiotic activity against Mycobacterium smegmatis MC2 155 and Mycobacterium tuberculosis H37Rv. Computer-aided molecular docking and 3D-QSAR can improve the design of new capuramycin antimycobacterial antibiotics
INT: Towards Infinite-frames 3D Detection with An Efficient Framework
It is natural to construct a multi-frame instead of a single-frame 3D
detector for a continuous-time stream. Although increasing the number of frames
might improve performance, previous multi-frame studies only used very limited
frames to build their systems due to the dramatically increased computational
and memory cost. To address these issues, we propose a novel on-stream training
and prediction framework that, in theory, can employ an infinite number of
frames while keeping the same amount of computation as a single-frame detector.
This infinite framework (INT), which can be used with most existing detectors,
is utilized, for example, on the popular CenterPoint, with significant latency
reductions and performance improvements. We've also conducted extensive
experiments on two large-scale datasets, nuScenes and Waymo Open Dataset, to
demonstrate the scheme's effectiveness and efficiency. By employing INT on
CenterPoint, we can get around 7% (Waymo) and 15% (nuScenes) performance boost
with only 2~4ms latency overhead, and currently SOTA on the Waymo 3D Detection
leaderboard.Comment: accepted by ECCV202
Simulation study on the optical processes at deep-sea neutrino telescope sites
The performance of a large-scale water Cherenkov neutrino telescope relies
heavily on the transparency of the surrounding water, quantified by its level
of light absorption and scattering. A pathfinder experiment was carried out to
measure the optical properties of deep seawater in South China Sea with
light-emitting diodes (LEDs) as light sources, photon multiplier tubes (PMTs)
and cameras as photon sensors. Here, we present an optical simulation program
employing the Geant4 toolkit to understand the absorption and scattering
processes in the deep seawater, which helps to extract the underlying optical
properties from the experimental data. The simulation results are compared with
the experimental data and show good agreements. We also verify the analysis
methods that utilize various observables of the PMTs and the cameras with this
simulation program, which can be easily adapted by other neutrino telescope
pathfinder experiments and future large-scale detectors.Comment: 27 pages, 11 figure
Melatonin-Mediated Sugar Accumulation and Growth Inhibition in Apple Plants Involves Down-Regulation of Fructokinase 2 Expression and Activity
Melatonin has been reported to play roles in regulating carbohydrate levels and plant growth. However, little is known about the exact mechanism by which melatonin regulates sugar levels and growth in plants. In this study, it was found that high levels of melatonin inhibited the growth of wild-type (WT) apple plants and induced significant accumulations of fructose, glucose, and sucrose in apple leaves, while MdFRK2 expression was significantly downregulated. MdFRK2 promoter transiently expressed in tobacco leaves further supported that the expression of MdFRK2 could be inhibited by exogenous melatonin. After applying exogenous melatonin, the suppression of MdFRK2 expression was significantly rescued in transgenic apples overexpressing MdFRK2 via the 35S promoter. Fructose, glucose, and sucrose concentrations increased less as compared to WT apple plants. Wild-type plants showed a stunted phenotype 21 days after melatonin treatment, while MdFRK2-overexpressing plants exhibited slightly inhibited growth, indicating that the downregulated MdFRK2 expression in response to melatonin was involved in melatonin-mediated growth inhibition. Taken together, these results demonstrate the involvement of MdFRK2 in melatonin-induced sugar accumulation and growth inhibition. Our findings shed light on the roles played by MdFRK2 in connecting melatonin action and plant growth
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