314 research outputs found
Smart Search Engine For Information Retrieval
This project addresses the main research problem in information retrieval and semantic search. It proposes the smart search theory as new theory based on hypothesis that semantic meanings of a document can be described by a set of
keywords. With two experiments designed and carried out in this project, the experiment result demonstrates positive evidence that meet the smart search theory.
In the theory proposed in this project, the smart search aims to determine a set of keywords for any web documents, by which the semantic meanings of the documents can be uniquely identified. Meanwhile, the size of the set of keywords is supposed to be small enough which can be easily managed. This is the fundamental assumption for creating the smart semantic search engine. In this project, the rationale of the assumption and the theory based on it will be discussed, as well as the processes of how the theory can be applied to the keyword allocation and the data model to be
generated. Then the design of the smart search engine will be proposed, in order to create a solution to the efficiency problem while searching among huge amount of increasing information published on the web.
To achieve high efficiency in web searching, statistical method is proved to be an effective way and it can be interpreted from the semantic level. Based on the frequency of joint keywords, the keyword list can be generated and linked to each other to form a meaning structure. A data model is built when a proper keyword list is achieved and the model is applied to the design of the smart search engine
Post-chemotherapy miR-146a expression and its prognostic potential in oral cancer patients
Purpose: To determine miR-146a expression level after chemotherapy in oral cancer patients, and itsprognostic value.Methods: Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used for the determination of miR-146 expression levels. Based on the results, the analysis of the miR-146a expression in oral cancer patients was performed by drawing ROC curve to provide information on the prognostic value of miR-146a. The survival of the patients was monitored over a period of 5 years. The patients were categorized into high- and low-expression groups, and multivariate Cox regression analysis method was adopted to provide a more comprehensive analysis of individual risk factors influencing the prognosis of oral cancer.Results: The miR-146a expression level in patients after chemotherapy was lower than that in patients before they received chemotherapy (p < 0.05). The specificity of using miR-146a to predict oral cancer was 76.83 %, the sensitivity 69.44 %, and the area between the curve and x-axis 0.78. In contrast, the survival level was significantly greater in high-expression patients (p < 0.05).Conclusion: The independent risk parameters for buccal carcinoma are drinking, smoking, chronic leukoplakia, and miR-146a
Why Factory1: The Spatial Signiļ¬cance of Architectural Education Buildings
The educational space of the Architecture faculty is used to cultivate architects. At the same time, it becomes the carrier of architectural ideas and teaching methods. The type of architecture and its spatial organization reļ¬ect the architectural education philosophy and attitude. Back in history, as early as the Renaissance, there had study places for architects emerged. After the industrial revolution and the modernist process, the types of architectural education sites are more diverse, and their main features are the spatial form of hybrid and box-in-box. This article preliminarily analyzes the evolution of the outline of architectural education building and interprets the spatial ideas in each period. The study focuses on the famous Dutch architectural schoolāBKCity of the Delft University of Technology, analyzing the teaching space logic of its distinctive Why Factory and exploring how the related space could stimulate the vitality of architectural education. By the analogy of some architectural schools, it also tries to compare the differences and characteristics of Chinese and Western architectural academies, ļ¬nding out the spatial signiļ¬cance in architecture discipline, education method as well as sustainable application
Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation
Designing new molecules is essential for drug discovery and material science.
Recently, deep generative models that aim to model molecule distribution have
made promising progress in narrowing down the chemical research space and
generating high-fidelity molecules. However, current generative models only
focus on modeling either 2D bonding graphs or 3D geometries, which are two
complementary descriptors for molecules. The lack of ability to jointly model
both limits the improvement of generation quality and further downstream
applications. In this paper, we propose a new joint 2D and 3D diffusion model
(JODO) that generates complete molecules with atom types, formal charges, bond
information, and 3D coordinates. To capture the correlation between molecular
graphs and geometries in the diffusion process, we develop a Diffusion Graph
Transformer to parameterize the data prediction model that recovers the
original data from noisy data. The Diffusion Graph Transformer interacts node
and edge representations based on our relational attention mechanism, while
simultaneously propagating and updating scalar features and geometric vectors.
Our model can also be extended for inverse molecular design targeting single or
multiple quantum properties. In our comprehensive evaluation pipeline for
unconditional joint generation, the results of the experiment show that JODO
remarkably outperforms the baselines on the QM9 and GEOM-Drugs datasets.
Furthermore, our model excels in few-step fast sampling, as well as in inverse
molecule design and molecular graph generation. Our code is provided in
https://github.com/GRAPH-0/JODO
Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehension
Although deep learning techniques have shown significant achievements, they
frequently depend on extensive amounts of hand-labeled data and tend to perform
inadequately in few-shot scenarios. The objective of this study is to devise a
strategy that can improve the model's capability to recognize biomedical
entities in scenarios of few-shot learning. By redefining biomedical named
entity recognition (BioNER) as a machine reading comprehension (MRC) problem,
we propose a demonstration-based learning method to address few-shot BioNER,
which involves constructing appropriate task demonstrations. In assessing our
proposed method, we compared the proposed method with existing advanced methods
using six benchmark datasets, including BC4CHEMD, BC5CDR-Chemical,
BC5CDR-Disease, NCBI-Disease, BC2GM, and JNLPBA. We examined the models'
efficacy by reporting F1 scores from both the 25-shot and 50-shot learning
experiments. In 25-shot learning, we observed 1.1% improvements in the average
F1 scores compared to the baseline method, reaching 61.7%, 84.1%, 69.1%, 70.1%,
50.6%, and 59.9% on six datasets, respectively. In 50-shot learning, we further
improved the average F1 scores by 1.0% compared to the baseline method,
reaching 73.1%, 86.8%, 76.1%, 75.6%, 61.7%, and 65.4%, respectively. We
reported that in the realm of few-shot learning BioNER, MRC-based language
models are much more proficient in recognizing biomedical entities compared to
the sequence labeling approach. Furthermore, our MRC-language models can
compete successfully with fully-supervised learning methodologies that rely
heavily on the availability of abundant annotated data. These results highlight
possible pathways for future advancements in few-shot BioNER methodologies
Towards Better Dynamic Graph Learning: New Architecture and Unified Library
We propose DyGFormer, a new Transformer-based architecture for dynamic graph
learning. DyGFormer is conceptually simple and only needs to learn from nodes'
historical first-hop interactions by: (1) a neighbor co-occurrence encoding
scheme that explores the correlations of the source node and destination node
based on their historical sequences; (2) a patching technique that divides each
sequence into multiple patches and feeds them to Transformer, allowing the
model to effectively and efficiently benefit from longer histories. We also
introduce DyGLib, a unified library with standard training pipelines,
extensible coding interfaces, and comprehensive evaluating protocols to promote
reproducible, scalable, and credible dynamic graph learning research. By
performing exhaustive experiments on thirteen datasets for dynamic link
prediction and dynamic node classification tasks, we find that DyGFormer
achieves state-of-the-art performance on most of the datasets, demonstrating
its effectiveness in capturing nodes' correlations and long-term temporal
dependencies. Moreover, some results of baselines are inconsistent with
previous reports, which may be caused by their diverse but less rigorous
implementations, showing the importance of DyGLib. All the used resources are
publicly available at https://github.com/yule-BUAA/DyGLib.Comment: Accepted at NeurIPS 202
Pretraining Language Models with Text-Attributed Heterogeneous Graphs
In many real-world scenarios (e.g., academic networks, social platforms),
different types of entities are not only associated with texts but also
connected by various relationships, which can be abstracted as Text-Attributed
Heterogeneous Graphs (TAHGs). Current pretraining tasks for Language Models
(LMs) primarily focus on separately learning the textual information of each
entity and overlook the crucial aspect of capturing topological connections
among entities in TAHGs. In this paper, we present a new pretraining framework
for LMs that explicitly considers the topological and heterogeneous information
in TAHGs. Firstly, we define a context graph as neighborhoods of a target node
within specific orders and propose a topology-aware pretraining task to predict
nodes involved in the context graph by jointly optimizing an LM and an
auxiliary heterogeneous graph neural network. Secondly, based on the
observation that some nodes are text-rich while others have little text, we
devise a text augmentation strategy to enrich textless nodes with their
neighbors' texts for handling the imbalance issue. We conduct link prediction
and node classification tasks on three datasets from various domains.
Experimental results demonstrate the superiority of our approach over existing
methods and the rationality of each design. Our code is available at
https://github.com/Hope-Rita/THLM.Comment: Accepted by EMNLP 2023 Finding
Semantic-Aware Dual Contrastive Learning for Multi-label Image Classification
Extracting image semantics effectively and assigning corresponding labels to
multiple objects or attributes for natural images is challenging due to the
complex scene contents and confusing label dependencies. Recent works have
focused on modeling label relationships with graph and understanding object
regions using class activation maps (CAM). However, these methods ignore the
complex intra- and inter-category relationships among specific semantic
features, and CAM is prone to generate noisy information. To this end, we
propose a novel semantic-aware dual contrastive learning framework that
incorporates sample-to-sample contrastive learning (SSCL) as well as
prototype-to-sample contrastive learning (PSCL). Specifically, we leverage
semantic-aware representation learning to extract category-related local
discriminative features and construct category prototypes. Then based on SSCL,
label-level visual representations of the same category are aggregated
together, and features belonging to distinct categories are separated.
Meanwhile, we construct a novel PSCL module to narrow the distance between
positive samples and category prototypes and push negative samples away from
the corresponding category prototypes. Finally, the discriminative label-level
features related to the image content are accurately captured by the joint
training of the above three parts. Experiments on five challenging large-scale
public datasets demonstrate that our proposed method is effective and
outperforms the state-of-the-art methods. Code and supplementary materials are
released on https://github.com/yu-gi-oh-leilei/SADCL.Comment: 8 pages, 6 figures, accepted by European Conference on Artificial
Intelligence (2023 ECAI
Self-optimizing Feature Generation via Categorical Hashing Representation and Hierarchical Reinforcement Crossing
Feature generation aims to generate new and meaningful features to create a
discriminative representation space.A generated feature is meaningful when the
generated feature is from a feature pair with inherent feature interaction. In
the real world, experienced data scientists can identify potentially useful
feature-feature interactions, and generate meaningful dimensions from an
exponentially large search space, in an optimal crossing form over an optimal
generation path. But, machines have limited human-like abilities.We generalize
such learning tasks as self-optimizing feature generation. Self-optimizing
feature generation imposes several under-addressed challenges on existing
systems: meaningful, robust, and efficient generation. To tackle these
challenges, we propose a principled and generic representation-crossing
framework to solve self-optimizing feature generation.To achieve hashing
representation, we propose a three-step approach: feature discretization,
feature hashing, and descriptive summarization. To achieve reinforcement
crossing, we develop a hierarchical reinforcement feature crossing approach.We
present extensive experimental results to demonstrate the effectiveness and
efficiency of the proposed method. The code is available at
https://github.com/yingwangyang/HRC_feature_cross.git
Predicting Temporal Sets with Deep Neural Networks
Given a sequence of sets, where each set contains an arbitrary number of
elements, the problem of temporal sets prediction aims to predict the elements
in the subsequent set. In practice, temporal sets prediction is much more
complex than predictive modelling of temporal events and time series, and is
still an open problem. Many possible existing methods, if adapted for the
problem of temporal sets prediction, usually follow a two-step strategy by
first projecting temporal sets into latent representations and then learning a
predictive model with the latent representations. The two-step approach often
leads to information loss and unsatisfactory prediction performance. In this
paper, we propose an integrated solution based on the deep neural networks for
temporal sets prediction. A unique perspective of our approach is to learn
element relationship by constructing set-level co-occurrence graph and then
perform graph convolutions on the dynamic relationship graphs. Moreover, we
design an attention-based module to adaptively learn the temporal dependency of
elements and sets. Finally, we provide a gated updating mechanism to find the
hidden shared patterns in different sequences and fuse both static and dynamic
information to improve the prediction performance. Experiments on real-world
data sets demonstrate that our approach can achieve competitive performances
even with a portion of the training data and can outperform existing methods
with a significant margin.Comment: 9 pages, 6 figures, Proceedings of the 26th ACM SIGKDD Conference on
Knowledge Discovery and Data Mining (KDD '2020
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