224 research outputs found
Semi-supervised MIMO Detection Using Cycle-consistent Generative Adversarial Network
In this paper, a new semi-supervised deep multiple-input multiple-output
(MIMO) detection approach using a cycle-consistent generative adversarial
network (CycleGAN) is proposed for communication systems without any prior
knowledge of underlying channel distributions. Specifically, we propose the
CycleGAN detector by constructing a bidirectional loop of two modified least
squares generative adversarial networks (LS-GAN). The forward LS-GAN learns to
model the transmission process, while the backward LS-GAN learns to detect the
received signals. By optimizing the cycle-consistency of the transmitted and
received signals through this loop, the proposed method is trained online and
semi-supervisedly using both the pilots and the received payload data. As such,
the demand on labelled training dataset is considerably controlled, and thus
the overhead is effectively reduced. Numerical results show that the proposed
CycleGAN detector achieves better performance in terms of both bit error-rate
(BER) and achievable rate than existing semi-blind deep learning (DL) detection
methods as well as conventional linear detectors, especially when considering
signal distortion due to the nonlinearity of power amplifiers (PA) at the
transmitter
Synthesis of dental resins using diatomite and nano-sized SiO2 and TiO2
AbstractThe mechanical properties of dental composites were improved by porous diatomite and nano-sized silica (OX-50) used as co-fillers. The resin composites, filled with silanized OX-50 and silanized diatomite (40:60wt/wt), presented the best flexural strength (133.1MPa), elastic modulus (9.5GPa) and Vickers microhardness (104.0HV). Besides these, TiO2 nanoparticles were introduced to tune the dental resin composites colours which were valued by the CIE-Lab system. The colour parameters (L⁎, a⁎, b⁎) showed that the colour changes of resin composites could be perceived obviously, when 300–400nm TiO2 particles were introduced as fillers. The resin composite, filled with 0.5wt% TiO2, exhibited both clear discolouration (ΔE⁎=3.22) and high mechanical strength. Using scanning electron microscope (SEM) equipped with an energy dispersive X-ray (EDX), the titanium elemental mapping results indicated that the TiO2 particles were distributed evenly in the prepared dental composites
Automated Prompting for Non-overlapping Cross-domain Sequential Recommendation
Cross-domain Recommendation (CR) has been extensively studied in recent years
to alleviate the data sparsity issue in recommender systems by utilizing
different domain information. In this work, we focus on the more general
Non-overlapping Cross-domain Sequential Recommendation (NCSR) scenario. NCSR is
challenging because there are no overlapped entities (e.g., users and items)
between domains, and there is only users' implicit feedback and no content
information. Previous CR methods cannot solve NCSR well, since (1) they either
need extra content to align domains or need explicit domain alignment
constraints to reduce the domain discrepancy from domain-invariant features,
(2) they pay more attention to users' explicit feedback (i.e., users' rating
data) and cannot well capture their sequential interaction patterns, (3) they
usually do a single-target cross-domain recommendation task and seldom
investigate the dual-target ones. Considering the above challenges, we propose
Prompt Learning-based Cross-domain Recommender (PLCR), an automated
prompting-based recommendation framework for the NCSR task. Specifically, to
address the challenge (1), PLCR resorts to learning domain-invariant and
domain-specific representations via its prompt learning component, where the
domain alignment constraint is discarded. For challenges (2) and (3), PLCR
introduces a pre-trained sequence encoder to learn users' sequential
interaction patterns, and conducts a dual-learning target with a separation
constraint to enhance recommendations in both domains. Our empirical study on
two sub-collections of Amazon demonstrates the advance of PLCR compared with
some related SOTA methods
Impacts of the Three Gorges Project on the Hydrological Regime in the Jingjiang Reach of the Yangtze River
Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv
Representation Learning with Ordered Relation Paths for Knowledge Graph Completion
Incompleteness is a common problem for existing knowledge graphs (KGs), and
the completion of KG which aims to predict links between entities is
challenging. Most existing KG completion methods only consider the direct
relation between nodes and ignore the relation paths which contain useful
information for link prediction. Recently, a few methods take relation paths
into consideration but pay less attention to the order of relations in paths
which is important for reasoning. In addition, these path-based models always
ignore nonlinear contributions of path features for link prediction. To solve
these problems, we propose a novel KG completion method named OPTransE. Instead
of embedding both entities of a relation into the same latent space as in
previous methods, we project the head entity and the tail entity of each
relation into different spaces to guarantee the order of relations in the path.
Meanwhile, we adopt a pooling strategy to extract nonlinear and complex
features of different paths to further improve the performance of link
prediction. Experimental results on two benchmark datasets show that the
proposed model OPTransE performs better than state-of-the-art methods
Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential Recommendation
Shared-account Cross-domain Sequential Recommendation (SCSR) task aims to
recommend the next item via leveraging the mixed user behaviors in multiple
domains. It is gaining immense research attention as more and more users tend
to sign up on different platforms and share accounts with others to access
domain-specific services. Existing works on SCSR mainly rely on mining
sequential patterns via Recurrent Neural Network (RNN)-based models, which
suffer from the following limitations: 1) RNN-based methods overwhelmingly
target discovering sequential dependencies in single-user behaviors. They are
not expressive enough to capture the relationships among multiple entities in
SCSR. 2) All existing methods bridge two domains via knowledge transfer in the
latent space, and ignore the explicit cross-domain graph structure. 3) None
existing studies consider the time interval information among items, which is
essential in the sequential recommendation for characterizing different items
and learning discriminative representations for them. In this work, we propose
a new graph-based solution, namely TiDA-GCN, to address the above challenges.
Specifically, we first link users and items in each domain as a graph. Then, we
devise a domain-aware graph convolution network to learn userspecific node
representations. To fully account for users' domainspecific preferences on
items, two effective attention mechanisms are further developed to selectively
guide the message passing process. Moreover, to further enhance item- and
account-level representation learning, we incorporate the time interval into
the message passing, and design an account-aware self-attention module for
learning items' interactive characteristics. Experiments demonstrate the
superiority of our proposed method from various aspects.Comment: 15 pages, 6 figure
Safety and Efficacy of a Novel Shunt Surgery Combined with Foam Sclerotherapy of Varices for Prehepatic Portal Hypertension: A Pilot Study
OBJECTIVES: This pilot study investigated the safety and efficacy of a novel shunt surgery combined with foam sclerotherapy of varices in patients with prehepatic portal hypertension. METHODS: Twenty-seven patients who were diagnosed with prehepatic portal hypertension and underwent shunt surgeries were divided into three groups by surgery type: shunt surgery alone (Group A), shunt surgery and devascularization (Group B), and shunt surgery combined with foam sclerotherapy (Group C). Between-group differences in operation time, intraoperative blood loss, portal pressure decrease, postoperative complications, rebleeding rates, encephalopathy, mortality rates and remission of gastroesophageal varices were compared. RESULTS: Groups A, B and C had similar operation times, intraoperative bleeding, and portal pressure decrease. The remission rates of varices differed significantly (po0.001): one patient in Group A and 6 patients in Group B had partial response, and all 9 patients in Group C had remission (2 complete, 7 partial). Two Group A patients and one Group B patient developed recurrent gastrointestinal bleeding postoperatively within 12 months. No postoperative recurrence or bleeding was observed in Group C, and no sclerotherapy-related complications were observed. CONCLUSIONS: Shunt surgery combined with foam sclerotherapy obliterates varices more effectively than shunt surgery alone does, decreasing the risk of postoperative rebleeding from residual gastroesophageal varices. This novel surgery is safe and effective with good short-term outcomes
Bufalin Induces Lung Cancer Cell Apoptosis via the Inhibition of PI3K/Akt Pathway
Bufalin is a class of toxic steroids which could induce the differentiation and apoptosis of leukemia cells, and induce the apoptosis of gastric, colon and breast cancer cells. However, the anti-tumor effects of bufalin have not been demonstrated in lung cancer. In this study we used A549 human lung adenocarcinoma epithelial cell line as the experimental model to evaluate the potential of bufalin in lung cancer chemotherapy. A549 cells were treated with bufalin, then the proliferation was detected by MTT assay and apoptosis was detected by flow cytometry analysis and Giemsa staining. In addition, A549 cells were treated by Akt inhibitor LY294002 in combination with bufalin and the activation of Akt and Caspase-3 as well as the expression levels of Bax, Bcl-2 and livin were examined by Western blot analysis. The results showed that Bufalin inhibited the proliferation of A549 cells and induced the apoptosis of A549 cells in a dose and time dependent manner. Mechanistically, we found that bufalin inhibited the activation of Akt. Moreover, bufalin synergized with Akt inhibitor to induce the apoptosis of A549 cells and this was associated with the upregulation of Bax expression, the downregulation of Bcl-2 and livin expression, and the activation of Caspase-3. In conclusion, our findings demonstrate that bufalin induces lung cancer cell apoptosis via the inhibition of PI3K/Akt pathway and suggest that bufalin is a potential regimen for combined chemotherapy to overcome the resistance of lung cancer cells to chemotherapeutics induced apoptosis
Enhancing Job Recommendation through LLM-based Generative Adversarial Networks
Recommending suitable jobs to users is a critical task in online recruitment
platforms, as it can enhance users' satisfaction and the platforms'
profitability. While existing job recommendation methods encounter challenges
such as the low quality of users' resumes, which hampers their accuracy and
practical effectiveness. With the rapid development of large language models
(LLMs), utilizing the rich external knowledge encapsulated within them, as well
as their powerful capabilities of text processing and reasoning, is a promising
way to complete users' resumes for more accurate recommendations. However,
directly leveraging LLMs to enhance recommendation results is not a
one-size-fits-all solution, as LLMs may suffer from fabricated generation and
few-shot problems, which degrade the quality of resume completion. In this
paper, we propose a novel LLM-based approach for job recommendation. To
alleviate the limitation of fabricated generation for LLMs, we extract accurate
and valuable information beyond users' self-description, which helps the LLMs
better profile users for resume completion. Specifically, we not only extract
users' explicit properties (e.g., skills, interests) from their
self-description but also infer users' implicit characteristics from their
behaviors for more accurate and meaningful resume completion. Nevertheless,
some users still suffer from few-shot problems, which arise due to scarce
interaction records, leading to limited guidance for the models in generating
high-quality resumes. To address this issue, we propose aligning unpaired
low-quality with high-quality generated resumes by Generative Adversarial
Networks (GANs), which can refine the resume representations for better
recommendation results. Extensive experiments on three large real-world
recruitment datasets demonstrate the effectiveness of our proposed method.Comment: 13 pages, 6 figures, 3 table
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