339 research outputs found
生物情報ネットワークのグラフ理論に基づく解析法
京都大学新制・課程博士博士(情報学)甲第24730号情博第818号新制||情||138(附属図書館)京都大学大学院情報学研究科知能情報学専攻(主査)教授 阿久津 達也, 教授 山本 章博, 教授 岡部 寿男学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Learning sophisticated feature interactions behind user behaviors is critical
in maximizing CTR for recommender systems. Despite great progress, existing
methods seem to have a strong bias towards low- or high-order interactions, or
require expertise feature engineering. In this paper, we show that it is
possible to derive an end-to-end learning model that emphasizes both low- and
high-order feature interactions. The proposed model, DeepFM, combines the power
of factorization machines for recommendation and deep learning for feature
learning in a new neural network architecture. Compared to the latest Wide \&
Deep model from Google, DeepFM has a shared input to its "wide" and "deep"
parts, with no need of feature engineering besides raw features. Comprehensive
experiments are conducted to demonstrate the effectiveness and efficiency of
DeepFM over the existing models for CTR prediction, on both benchmark data and
commercial data
CTRL: Connect Tabular and Language Model for CTR Prediction
Traditional click-through rate (CTR) prediction models convert the tabular
data into one-hot vectors and leverage the collaborative relations among
features for inferring user's preference over items. This modeling paradigm
discards the essential semantic information. Though some recent works like P5
and M6-Rec have explored the potential of using Pre-trained Language Models
(PLMs) to extract semantic signals for CTR prediction, they are computationally
expensive and suffer from low efficiency. Besides, the beneficial collaborative
relations are not considered, hindering the recommendation performance. To
solve these problems, in this paper, we propose a novel framework
\textbf{CTRL}, which is industrial friendly and model-agnostic with high
training and inference efficiency. Specifically, the original tabular data is
first converted into textual data. Both tabular data and converted textual data
are regarded as two different modalities and are separately fed into the
collaborative CTR model and pre-trained language model. A cross-modal knowledge
alignment procedure is performed to fine-grained align and integrate the
collaborative and semantic signals, and the lightweight collaborative model can
be deployed online for efficient serving after fine-tuned with supervised
signals. Experimental results on three public datasets show that CTRL
outperforms the SOTA CTR models significantly. Moreover, we further verify its
effectiveness on a large-scale industrial recommender system
The stability and instability of the language control network: a longitudinal resting-state functional magnetic resonance imaging study
The language control network is vital among language-related networks
responsible for solving the problem of multiple language switching. Researchers
have expressed concerns about the instability of the language control network
when exposed to external influences (e.g., Long-term second language learning).
However, some studies have suggested that the language control network is
stable. Therefore, whether the language control network is stable or not
remains unclear. In the present study, we directly evaluated the stability and
instability of the language control network using resting-state functional
magnetic resonance imaging (rs-fMRI). We employed cohorts of Chinese first-year
college students majoring in English who underwent second language (L2)
acquisition courses at a university and those who did not. Two resting-state
fMRI scans were acquired approximately 1 year apart. We found that the language
control network was both moderately stable and unstable. We further
investigated the morphological coexistence patterns of stability and
instability within the language control network. First, we extracted
connections representing stability and plasticity from the entire network. We
then evaluated whether the coexistence patterns were modular (stability and
instability involve different brain regions) or non-modular (stability and
plasticity involve the same brain regions but have unique connectivity
patterns). We found that both stability and instability coexisted in a
non-modular pattern. Compared with the non-English major group, the English
major group has a more non-modular coexistence pattern.. These findings provide
preliminary evidence of the coexistence of stability and instability in the
language control network
New and Improved Algorithms for Unordered Tree Inclusion
The tree inclusion problem is, given two node-labeled trees P and T (the "pattern tree" and the "text tree"), to locate every minimal subtree in T (if any) that can be obtained by applying a sequence of node insertion operations to P. Although the ordered tree inclusion problem is solvable in polynomial time, the unordered tree inclusion problem is NP-hard. The currently fastest algorithm for the latter is from 1995 and runs in O(poly(m,n) * 2^{2d}) = O^*(2^{2d}) time, where m and n are the sizes of the pattern and text trees, respectively, and d is the maximum outdegree of the pattern tree. Here, we develop a new algorithm that improves the exponent 2d to d by considering a particular type of ancestor-descendant relationships and applying dynamic programming, thus reducing the time complexity to O^*(2^d). We then study restricted variants of the unordered tree inclusion problem where the number of occurrences of different node labels and/or the input trees\u27 heights are bounded. We show that although the problem remains NP-hard in many such cases, it can be solved in polynomial time for c = 2 and in O^*(1.8^d) time for c = 3 if the leaves of P are distinctly labeled and each label occurs at most c times in T. We also present a randomized O^*(1.883^d)-time algorithm for the case that the heights of P and T are one and two, respectively
Densest subgraph-based methods for protein-protein interaction hot spot prediction
[Background] Hot spots play an important role in protein binding analysis. The residue interaction network is a key point in hot spot prediction, and several graph theory-based methods have been proposed to detect hot spots. Although the existing methods can yield some interesting residues by network analysis, low recall has limited their abilities in finding more potential hot spots. [Result] In this study, we develop three graph theory-based methods to predict hot spots from only a single residue interaction network. We detect the important residues by finding subgraphs with high densities, i.e., high average degrees. Generally, a high degree implies a high binding possibility between protein chains, and thus a subgraph with high density usually relates to binding sites that have a high rate of hot spots. By evaluating the results on 67 complexes from the SKEMPI database, our methods clearly outperform existing graph theory-based methods on recall and F-score. In particular, our main method, Min-SDS, has an average recall of over 0.665 and an f2-score of over 0.364, while the recall and f2-score of the existing methods are less than 0.400 and 0.224, respectively. [Conclusion] The Min-SDS method performs best among all tested methods on the hot spot prediction problem, and all three of our methods provide useful approaches for analyzing bionetworks. In addition, the densest subgraph-based methods predict hot spots with only one residue interaction network, which is constructed from spatial atomic coordinate data to mitigate the shortage of data from wet-lab experiments
Dynamic Structured Illumination Microscopy with a Neural Space-time Model
Structured illumination microscopy (SIM) reconstructs a super-resolved image
from multiple raw images captured with different illumination patterns; hence,
acquisition speed is limited, making it unsuitable for dynamic scenes. We
propose a new method, Speckle Flow SIM, that uses static patterned illumination
with moving samples and models the sample motion during data capture in order
to reconstruct the dynamic scene with super-resolution. Speckle Flow SIM relies
on sample motion to capture a sequence of raw images. The spatio-temporal
relationship of the dynamic scene is modeled using a neural space-time model
with coordinate-based multi-layer perceptrons (MLPs), and the motion dynamics
and the super-resolved scene are jointly recovered. We validate Speckle Flow
SIM for coherent imaging in simulation and build a simple, inexpensive
experimental setup with off-the-shelf components. We demonstrate that Speckle
Flow SIM can reconstruct a dynamic scene with deformable motion and 1.88x the
diffraction-limited resolution in experiment
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