201 research outputs found
Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling
Recommender systems are essential for online applications, and sequential
recommendation has enjoyed significant prevalence due to its expressive ability
to capture dynamic user interests. However, previous sequential modeling
methods still have limitations in capturing contextual information. The primary
reason for this issue is that language models often lack an understanding of
domain-specific knowledge and item-related textual content. To address this
issue, we adopt a new sequential recommendation paradigm and propose LANCER,
which leverages the semantic understanding capabilities of pre-trained language
models to generate personalized recommendations. Our approach bridges the gap
between language models and recommender systems, resulting in more human-like
recommendations. We demonstrate the effectiveness of our approach through
experiments on several benchmark datasets, showing promising results and
providing valuable insights into the influence of our model on sequential
recommendation tasks. Furthermore, our experimental codes are publicly
available
Using simulated Tianqin gravitational wave data and electromagnetic wave data to study the coincidence problem and Hubble tension problem
In this paper, we use electromagnetic wave data (H0LiCOW, , SNe) and
gravitational wave data (Tianqin) to constrain the interacting dark energy
(IDE) model and investigate the Hubble tension problem and coincidences
problem. By combining these four kinds of data (Tianqin+H0LiCOW+SNe+), we
obtained the parameter values at the confidence interval of :
, ,
, and . According
to our results, the best valve of show that the Hubble tension problem
can be alleviated to some extent. In addition, the of which the center value indicates the
coincidence problem is slightly alleviated. However, the is
still within the error range which indicates the CDM model
is still the model which is in best agreement with the observational data at
present. Finally, we compare the constraint results of electromagnetic wave and
gravitational wave on the model parameters and find that the constraint effect
of electromagnetic wave data on model parameters is better than that of
simulated Tianqin gravitational wave data.Comment: The article has been accepted by Chinese Physics
Nested Named Entity Recognition from Medical Texts: An Adaptive Shared Network Architecture with Attentive CRF
Recognizing useful named entities plays a vital role in medical information
processing, which helps drive the development of medical area research. Deep
learning methods have achieved good results in medical named entity recognition
(NER). However, we find that existing methods face great challenges when
dealing with the nested named entities. In this work, we propose a novel
method, referred to as ASAC, to solve the dilemma caused by the nested
phenomenon, in which the core idea is to model the dependency between different
categories of entity recognition. The proposed method contains two key modules:
the adaptive shared (AS) part and the attentive conditional random field (ACRF)
module. The former part automatically assigns adaptive weights across each task
to achieve optimal recognition accuracy in the multi-layer network. The latter
module employs the attention operation to model the dependency between
different entities. In this way, our model could learn better entity
representations by capturing the implicit distinctions and relationships
between different categories of entities. Extensive experiments on public
datasets verify the effectiveness of our method. Besides, we also perform
ablation analyses to deeply understand our methods
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Optical biopsy identification and grading of gliomas using label-free visible resonance Raman spectroscopy.
Glioma is one of the most refractory types of brain tumor. Accurate tumor boundary identification and complete resection of the tumor are essential for glioma removal during brain surgery. We present a method based on visible resonance Raman (VRR) spectroscopy to identify glioma margins and grades. A set of diagnostic spectral biomarkers features are presented based on tissue composition changes revealed by VRR. The Raman spectra include molecular vibrational fingerprints of carotenoids, tryptophan, amide I/II/III, proteins, and lipids. These basic in situ spectral biomarkers are used to identify the tissue from the interface between brain cancer and normal tissue and to evaluate glioma grades. The VRR spectra are also analyzed using principal component analysis for dimension reduction and feature detection and support vector machine for classification. The cross-validated sensitivity, specificity, and accuracy are found to be 100%, 96.3%, and 99.6% to distinguish glioma tissues from normal brain tissues, respectively. The area under the receiver operating characteristic curve for the classification is about 1.0. The accuracies to distinguish normal, low grade (grades I and II), and high grade (grades III and IV) gliomas are found to be 96.3%, 53.7%, and 84.1% for the three groups, respectively, along with a total accuracy of 75.1%. A set of criteria for differentiating normal human brain tissues from normal control tissues is proposed and used to identify brain cancer margins, yielding a diagnostic sensitivity of 100% and specificity of 71%. Our study demonstrates the potential of VRR as a label-free optical molecular histopathology method used for in situ boundary line judgment for brain surgery in the margins
TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders
Enhancing the expressive capacity of deep learning-based time series models
with self-supervised pre-training has become ever-increasingly prevalent in
time series classification. Even though numerous efforts have been devoted to
developing self-supervised models for time series data, we argue that the
current methods are not sufficient to learn optimal time series representations
due to solely unidirectional encoding over sparse point-wise input units. In
this work, we propose TimeMAE, a novel self-supervised paradigm for learning
transferrable time series representations based on transformer networks. The
distinct characteristics of the TimeMAE lie in processing each time series into
a sequence of non-overlapping sub-series via window-slicing partitioning,
followed by random masking strategies over the semantic units of localized
sub-series. Such a simple yet effective setting can help us achieve the goal of
killing three birds with one stone, i.e., (1) learning enriched contextual
representations of time series with a bidirectional encoding scheme; (2)
increasing the information density of basic semantic units; (3) efficiently
encoding representations of time series using transformer networks.
Nevertheless, it is a non-trivial to perform reconstructing task over such a
novel formulated modeling paradigm. To solve the discrepancy issue incurred by
newly injected masked embeddings, we design a decoupled autoencoder
architecture, which learns the representations of visible (unmasked) positions
and masked ones with two different encoder modules, respectively. Furthermore,
we construct two types of informative targets to accomplish the corresponding
pretext tasks. One is to create a tokenizer module that assigns a codeword to
each masked region, allowing the masked codeword classification (MCC) task to
be completed effectively...Comment: Submitted to IEEE TRANSACTIONS ON KNOWLEDGE AND DATA
ENGINEERING(TKDE), under revie
Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights
Adapters, a plug-in neural network module with some tunable parameters, have
emerged as a parameter-efficient transfer learning technique for adapting
pre-trained models to downstream tasks, especially for natural language
processing (NLP) and computer vision (CV) fields. Meanwhile, learning
recommendation models directly from raw item modality features -- e.g., texts
of NLP and images of CV -- can enable effective and transferable recommender
systems (called TransRec). In view of this, a natural question arises: can
adapter-based learning techniques achieve parameter-efficient TransRec with
good performance?
To this end, we perform empirical studies to address several key
sub-questions. First, we ask whether the adapter-based TransRec performs
comparably to TransRec based on standard full-parameter fine-tuning? does it
hold for recommendation with different item modalities, e.g., textual RS and
visual RS. If yes, we benchmark these existing adapters, which have been shown
to be effective in NLP and CV tasks, in the item recommendation settings.
Third, we carefully study several key factors for the adapter-based TransRec in
terms of where and how to insert these adapters? Finally, we look at the
effects of adapter-based TransRec by either scaling up its source training data
or scaling down its target training data. Our paper provides key insights and
practical guidance on unified & transferable recommendation -- a less studied
recommendation scenario. We promise to release all code & datasets for future
research
A survey of path planning of industrial robots based on rapidly exploring random trees
Path planning is an essential part of robot intelligence. In this paper, we summarize the characteristics of path planning of industrial robots. And owing to the probabilistic completeness, we review the rapidly-exploring random tree (RRT) algorithm which is widely used in the path planning of industrial robots. Aiming at the shortcomings of the RRT algorithm, this paper investigates the RRT algorithm for path planning of industrial robots in order to improve its intelligence. Finally, the future development direction of the RRT algorithm for path planning of industrial robots is proposed. The study results have particularly guided significance for the development of the path planning of industrial robots and the applicability and practicability of the RRT algorithm
A novel method of weakness imbalance fault identification and application in aero-hydraulic pump
A method of combining auto-correlation and Hilbert envelope analysis is proposed and used to identify weakness imbalance fault of aero-hydraulic pump, the central part of hydraulic system of aircraft. Firstly, the integral and polynomial least square fitting is applied to convert acceleration signal to velocity one; secondly, the Hilbert envelope spectrum of auto-correlation function of velocity signal is obtained and used to identify the weakness imbalance fault of aero-hydraulic pump; finally, the energy ratio of velocity signal is calculated according to Hilbert envelope spectrum for identifying imbalance fault of aero-hydraulic pump by means of easier and more visual method. Meanwhile, the comparing analysis is carried out between traditional research method and proposed new one. The result shows that the weakness imbalance fault of aero-hydraulic pump can be identified and diagnosed effectively and correctly according to the velocity signal whether Hilbert envelope spectrum or calculation energy ratio while direct acceleration signal cannot
Fluid Retention Caused by Rosiglitazone Is Related to Increases in AQP2 and α
Peroxisome proliferator activated receptor-γ (PPARγ) is a ligand-activated transcription factor of the nuclear hormone receptor superfamily. The decreased phosphorylation of PPARγ due to rosiglitazone (ROS) is the main reason for the increased insulin sensitivity caused by this antidiabetic drug. However, there is no clear evidence whether the nuclear translocation of p-PPARγ stimulated by ROS is related to fluid retention. It is also unclear whether the translocation of p-PPARγ is associated with the change of aquaporin-2 (AQP2) and epithelial sodium channel α subunit (αENaC) in membranes, cytoplasm, and nucleus. Our experiments indicate that ROS significantly downregulates nuclear p-PPARγ and increases membrane AQP2 and αENaC; however, SR1664 (a nonagonist PPARγ ligand) reduces p-PPARγ and has no effect on AQP2 and αENaC. Therefore, we conclude that in vitro the fluid retention caused by ROS is associated with the increases in membrane αENaC and AQP2 but has little relevance to the phosphorylation of PPARγ
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