1,614 research outputs found
UFIN: Universal Feature Interaction Network for Multi-Domain Click-Through Rate Prediction
Click-Through Rate (CTR) prediction, which aims to estimate the probability
of a user clicking on an item, is a key task in online advertising. Numerous
existing CTR models concentrate on modeling the feature interactions within a
solitary domain, thereby rendering them inadequate for fulfilling the
requisites of multi-domain recommendations in real industrial scenarios. Some
recent approaches propose intricate architectures to enhance knowledge sharing
and augment model training across multiple domains. However, these approaches
encounter difficulties when being transferred to new recommendation domains,
owing to their reliance on the modeling of ID features (e.g., item id). To
address the above issue, we propose the Universal Feature Interaction Network
(UFIN) approach for CTR prediction. UFIN exploits textual data to learn
universal feature interactions that can be effectively transferred across
diverse domains. For learning universal feature representations, we regard the
text and feature as two different modalities and propose an encoder-decoder
network founded on a Large Language Model (LLM) to enforce the transfer of data
from the text modality to the feature modality. Building upon the above
foundation, we further develop a mixtureof-experts (MoE) enhanced adaptive
feature interaction model to learn transferable collaborative patterns across
multiple domains. Furthermore, we propose a multi-domain knowledge distillation
framework to enhance feature interaction learning. Based on the above methods,
UFIN can effectively bridge the semantic gap to learn common knowledge across
various domains, surpassing the constraints of ID-based models. Extensive
experiments conducted on eight datasets show the effectiveness of UFIN, in both
multidomain and cross-platform settings. Our code is available at
https://github.com/RUCAIBox/UFIN
Failure Mode and Ductility of Dual Phase Steel with Edge Crack
AbstractDual phase steels having a microstructure consisting of a ferrite matrix, in which particles of martensite are dispersed, have received a great deal of attention due to their useful combination of high strength, high work hardening rate and ductility, all of which are favorable properties for forming processes. In the present work, various microstructure-level finite element models are generated based on the actual microstructure of DP590 steel, to capture the mechanical behavior and fracture mode. The failure mode of DP steels is predicted using the plastic strain localization theory, mainly resulting from the material microstructure-level inhomogeneity as well as the initial geometrical imperfection. Besides the simulation, tensile test specimens of dog bone type with different edge cracks were prepared on an internally designed blanking tool, and the corresponding deformation processes were recorded via digital image correlation system. It is found that the overall ductility of the DP590 steel strongly depends on the ductility of the ferrite matrix, and pre-existing edge cracks reduce the overall ductility of the steel and change the failure mode
N′-(Butan-2-ylidene)furan-2-carbohydrazide
The title Schiff base compound, C9H12N2O2, was obtained from a condensation reaction of butan-2-one and furan-2-carbohydrazide. The furan ring and the hydrazide fragment are roughly planar, the largest deviation from the mean plane being 0.069 (2)Å, but the butanylidene group is twisted slightly with respect to this plane by a dihedral angle of 5.2 (3)°. In the crystal, intermolecular N—H⋯O hydrogen bonds link pairs of inversion-related molecules, forming dimers of R
2
2(8) graph-set motif
EulerNet: Adaptive Feature Interaction Learning via Euler's Formula for CTR Prediction
Learning effective high-order feature interactions is very crucial in the CTR
prediction task. However, it is very time-consuming to calculate high-order
feature interactions with massive features in online e-commerce platforms. Most
existing methods manually design a maximal order and further filter out the
useless interactions from them. Although they reduce the high computational
costs caused by the exponential growth of high-order feature combinations, they
still suffer from the degradation of model capability due to the suboptimal
learning of the restricted feature orders. The solution to maintain the model
capability and meanwhile keep it efficient is a technical challenge, which has
not been adequately addressed. To address this issue, we propose an adaptive
feature interaction learning model, named as EulerNet, in which the feature
interactions are learned in a complex vector space by conducting space mapping
according to Euler's formula. EulerNet converts the exponential powers of
feature interactions into simple linear combinations of the modulus and phase
of the complex features, making it possible to adaptively learn the high-order
feature interactions in an efficient way. Furthermore, EulerNet incorporates
the implicit and explicit feature interactions into a unified architecture,
which achieves the mutual enhancement and largely boosts the model
capabilities. Such a network can be fully learned from data, with no need of
pre-designed form or order for feature interactions. Extensive experiments
conducted on three public datasets have demonstrated the effectiveness and
efficiency of our approach. Our code is available at:
https://github.com/RUCAIBox/EulerNet.Comment: 10 pages, 7 figures, accepted for publication in SIGIR'2
Identification of a potential novel biomarker in intervertebral disk degeneration by bioinformatics analysis and experimental validation
BackgroundIntervertebral disk degeneration (IVDD) is a major cause of low back pain and one of the most common health problems all over the world. However, the early diagnosis of IVDD is still restricted. The purpose of this study is to identify and validate the key characteristic gene of IVDD and analyze its correlation with immune cell infiltration.Methods3 IVDD-related gene expression profiles were downloaded from the Gene Expression Omnibus database to screen for differentially expressed genes (DEGs). Gene Ontology (GO) and gene set enrichment analysis (GSEA) were conducted to explore the biological functions. Two machine learning algorithms were used to identify characteristic genes, which were tested to further find the key characteristic gene. The receiver operating characteristic curve was performed to estimate the clinical diagnostic value of the key characteristic gene. The excised human intervertebral disks were obtained, and the normal nucleus pulposus (NP) and degenerative NP were carefully separated and cultured in vitro. The expression of the key characteristic gene was validated by real-time quantitative PCR (qRT-PCR). The related protein expression in NP cells was detected by Western blot. Finally, the correlation was investigated between the key characteristic gene and immune cell infiltration.ResultsA total of 5 DEGs, including 3 upregulated genes and 2 downregulated genes, were screened between IVDD and control samples. GO enrichment analysis showed that DEGs were enriched to 4 items in BP, 6 items in CC, and 13 items in MF. They mainly included the regulation of ion transmembrane transport, transporter complex, and channel activity. GSEA suggested that the cell cycle, DNA replication, graft versus host disease, and nucleotide excision repair were enriched in control samples, while complement and coagulation cascades, Fc γ R–mediated phagocytosis, neuroactive ligand–receptor interaction, the NOD-like receptor signaling pathway, gap junctions, etc., were enriched in IVDD samples. Furthermore, ZNF542P was identified and tested as key characteristic gene in IVDD samples through machine learning algorithms and showed a good diagnostic value. The results of qRT-PCR showed that compared with normal NP cells, the expression of ZNF542P gene was decreased in degenerated NP cells. The results of Western blot suggested that compared with normal NP cells, the expression of NLRP3 and pro Caspase-1 was increased in degenerated NP cells. Finally, we found that the expression of ZNF542P was positively related to the proportions of T cells gamma delta (γδT cells).ConclusionZNF542P is a potential biomarker in the early diagnosis of IVDD and may be associated with the NOD-like receptor signaling pathway and the infiltration of γδT cells
5 GHz TMRT observations of 71 pulsars
We present integrated pulse profiles at 5~GHz for 71 pulsars, including eight
millisecond pulsars (MSPs), obtained using the Shanghai Tian Ma Radio Telescope
(TMRT). Mean flux densities and pulse widths are measured. For 19 normal
pulsars and one MSP, these are the first detections at 5~GHz and for a further
19, including five MPSs, the profiles have a better signal-to-noise ratio than
previous observations. Mean flux density spectra between 400~MHz and 9~GHz are
presented for 27 pulsars and correlations of power-law spectral index are found
with characteristic age, radio pseudo-luminosity and spin-down luminosity. Mode
changing was detected in five pulsars. The separation between the main pulse
and interpulse is shown to be frequency independent for six pulsars but a
frequency dependence of the relative intensity of the main pulse and interpulse
is found. The frequency dependence of component separations is investigated for
20 pulsars and three groups are found: in seven cases the separation between
the outmost leading and trailing components decreases with frequency, roughly
in agreement with radius-to-frequency mapping; in eleven cases the separation
is nearly constant; in the remain two cases the separation between the outmost
components increases with frequency. We obtain the correlations of pulse widths
with pulsar period and estimate the core widths of 23 multi-component profiles
and conal widths of 17 multi-component profiles at 5.0~GHz using Gaussian
fitting and discuss the width-period relationship at 5~GHz compared with the
results at at 1.0~GHz and 8.6~GHz.Comment: 46 pages, 14 figures, 8 Tables, accepted by Ap
Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction
Target-oriented opinion words extraction (TOWE) is a new subtask of ABSA,
which aims to extract the corresponding opinion words for a given opinion
target in a sentence. Recently, neural network methods have been applied to
this task and achieve promising results. However, the difficulty of annotation
causes the datasets of TOWE to be insufficient, which heavily limits the
performance of neural models. By contrast, abundant review sentiment
classification data are easily available at online review sites. These reviews
contain substantial latent opinions information and semantic patterns. In this
paper, we propose a novel model to transfer these opinions knowledge from
resource-rich review sentiment classification datasets to low-resource task
TOWE. To address the challenges in the transfer process, we design an effective
transformation method to obtain latent opinions, then integrate them into TOWE.
Extensive experimental results show that our model achieves better performance
compared to other state-of-the-art methods and significantly outperforms the
base model without transferring opinions knowledge. Further analysis validates
the effectiveness of our model.Comment: Accepted by the 34th AAAI Conference on Artificial Intelligence (AAAI
2020
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