51 research outputs found
Efficient Last-iterate Convergence Algorithms in Solving Games
No-regret algorithms are popular for learning Nash equilibrium (NE) in
two-player zero-sum normal-form games (NFGs) and extensive-form games (EFGs).
Many recent works consider the last-iterate convergence no-regret algorithms.
Among them, the two most famous algorithms are Optimistic Gradient Descent
Ascent (OGDA) and Optimistic Multiplicative Weight Update (OMWU). However, OGDA
has high per-iteration complexity. OMWU exhibits a lower per-iteration
complexity but poorer empirical performance, and its convergence holds only
when NE is unique. Recent works propose a Reward Transformation (RT) framework
for MWU, which removes the uniqueness condition and achieves competitive
performance with OMWU. Unfortunately, RT-based algorithms perform worse than
OGDA under the same number of iterations, and their convergence guarantee is
based on the continuous-time feedback assumption, which does not hold in most
scenarios. To address these issues, we provide a closer analysis of the RT
framework, which holds for both continuous and discrete-time feedback. We
demonstrate that the essence of the RT framework is to transform the problem of
learning NE in the original game into a series of strongly convex-concave
optimization problems (SCCPs). We show that the bottleneck of RT-based
algorithms is the speed of solving SCCPs. To improve the their empirical
performance, we design a novel transformation method to enable the SCCPs can be
solved by Regret Matching+ (RM+), a no-regret algorithm with better empirical
performance, resulting in Reward Transformation RM+ (RTRM+). RTRM+ enjoys
last-iterate convergence under the discrete-time feedback setting. Using the
counterfactual regret decomposition framework, we propose Reward Transformation
CFR+ (RTCFR+) to extend RTRM+ to EFGs. Experimental results show that our
algorithms significantly outperform existing last-iterate convergence
algorithms and RM+ (CFR+)
Bootstrapping Multi-view Representations for Fake News Detection
Previous researches on multimedia fake news detection include a series of
complex feature extraction and fusion networks to gather useful information
from the news. However, how cross-modal consistency relates to the fidelity of
news and how features from different modalities affect the decision-making are
still open questions. This paper presents a novel scheme of Bootstrapping
Multi-view Representations (BMR) for fake news detection. Given a multi-modal
news, we extract representations respectively from the views of the text, the
image pattern and the image semantics. Improved Multi-gate Mixture-of-Expert
networks (iMMoE) are proposed for feature refinement and fusion.
Representations from each view are separately used to coarsely predict the
fidelity of the whole news, and the multimodal representations are able to
predict the cross-modal consistency. With the prediction scores, we reweigh
each view of the representations and bootstrap them for fake news detection.
Extensive experiments conducted on typical fake news detection datasets prove
that the proposed BMR outperforms state-of-the-art schemes.Comment: Authors are from Fudan University, China. Under Revie
Complexes between C-Reactive Protein and Very Low Density Lipoprotein Delay Bacterial Clearance in Sepsis
Precision Higgs physics at the CEPC
The discovery of the Higgs boson with its mass around 125 GeV by the ATLAS
and CMS Collaborations marked the beginning of a new era in high energy
physics. The Higgs boson will be the subject of extensive studies of the
ongoing LHC program. At the same time, lepton collider based Higgs factories
have been proposed as a possible next step beyond the LHC, with its main goal
to precisely measure the properties of the Higgs boson and probe potential new
physics associated with the Higgs boson. The Circular Electron Positron
Collider~(CEPC) is one of such proposed Higgs factories. The CEPC is an
circular collider proposed by and to be hosted in China. Located in a
tunnel of approximately 100~km in circumference, it will operate at a
center-of-mass energy of 240~GeV as the Higgs factory. In this paper, we
present the first estimates on the precision of the Higgs boson property
measurements achievable at the CEPC and discuss implications of these
measurements.Comment: 46 pages, 37 figure
An Efficient Deep Reinforcement Learning Algorithm for Solving Imperfect Information Extensive-Form Games
One of the most popular methods for learning Nash equilibrium (NE) in large-scale imperfect information extensive-form games (IIEFGs) is the neural variants of counterfactual regret minimization (CFR). CFR is a special case of Follow-The-Regularized-Leader (FTRL). At each iteration, the neural variants of CFR update the agent's strategy via the estimated counterfactual regrets. Then, they use neural networks to approximate the new strategy, which incurs an approximation error. These approximation errors will accumulate since the counterfactual regrets at iteration t are estimated using the agent's past approximated strategies. Such accumulated approximation error causes poor performance. To address this accumulated approximation error, we propose a novel FTRL algorithm called FTRL-ORW, which does not utilize the agent's past strategies to pick the next iteration strategy. More importantly, FTRL-ORW can update its strategy via the trajectories sampled from the game, which is suitable to solve large-scale IIEFGs since sampling multiple actions for each information set is too expensive in such games. However, it remains unclear which algorithm to use to compute the next iteration strategy for FTRL-ORW when only such sampled trajectories are revealed at iteration t. To address this problem and scale FTRL-ORW to large-scale games, we provide a model-free method called Deep FTRL-ORW, which computes the next iteration strategy using model-free Maximum Entropy Deep Reinforcement Learning. Experimental results on two-player zero-sum IIEFGs show that Deep FTRL-ORW significantly outperforms existing model-free neural methods and OS-MCCFR
Generative Steganographic Flow
Generative steganography (GS) is a new data hiding manner, featuring direct
generation of stego media from secret data. Existing GS methods are generally
criticized for their poor performances. In this paper, we propose a novel flow
based GS approach -- Generative Steganographic Flow (GSF), which provides
direct generation of stego images without cover image. We take the stego image
generation and secret data recovery process as an invertible transformation,
and build a reversible bijective mapping between input secret data and
generated stego images. In the forward mapping, secret data is hidden in the
input latent of Glow model to generate stego images. By reversing the mapping,
hidden data can be extracted exactly from generated stego images. Furthermore,
we propose a novel latent optimization strategy to improve the fidelity of
stego images. Experimental results show our proposed GSF has far better
performances than SOTA works.Comment: The accepted paper in ICME 202
The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma
Abstract Background This study aims to construct radiomics models based on [18F]FDG PET/CT using multiple machine learning methods to predict the EGFR mutation status of lung adenocarcinoma and evaluate whether incorporating clinical parameters can improve the performance of radiomics models. Methods A total of 515 patients were retrospectively collected and divided into a training set (n = 404) and an independent testing set (n = 111) according to their examination time. After semi-automatic segmentation of PET/CT images, the radiomics features were extracted, and the best feature sets of CT, PET, and PET/CT modalities were screened out. Nine radiomics models were constructed using logistic regression (LR), random forest (RF), and support vector machine (SVM) methods. According to the performance in the testing set, the best model of the three modalities was kept, and its radiomics score (Rad-score) was calculated. Furthermore, combined with the valuable clinical parameters (gender, smoking history, nodule type, CEA, SCC-Ag), a joint radiomics model was built. Results Compared with LR and SVM, the RF Rad-score showed the best performance among the three radiomics models of CT, PET, and PET/CT (training and testing sets AUC: 0.688, 0.666, and 0.698 vs. 0.726, 0.678, and 0.704). Among the three joint models, the PET/CT joint model performed the best (training and testing sets AUC: 0.760 vs. 0.730). The further stratified analysis found that CT_RF had the best prediction effect for stage I–II lesions (training set and testing set AUC: 0.791 vs. 0.797), while PET/CT joint model had the best prediction effect for stage III–IV lesions (training and testing sets AUC: 0.722 vs. 0.723). Conclusions Combining with clinical parameters can improve the predictive performance of PET/CT radiomics model, especially for patients with advanced lung adenocarcinoma
High triglyceride levels increase the risk of diabetic microvascular complications: a cross-sectional study
Abstract Background The prevalence of microvascular complications in type 2 diabetes mellitus (T2DM) is increasing. The effect of lipid profiles on diabetic microvascular complications remains debated. This research aimed to study the correlation between lipid profiles and microvascular complications. Methods This retrospective cross-sectional study included 1096 T2DM patients. The patients were divided into the control, diabetic retinopathy (DR), nephropathy (DKD), and peripheral neuropathy (DPN) groups based on the existence of corresponding complications. The lipid profiles were analyzed, and the effect on complications was assessed by logistic regression. Results Compared with the control group, the diabetic microvascular complications group had a higher dyslipidemia rate. The rate of high TGs increased significantly with an increasing number of complications. High TG levels contributed to the risk of DKD, DR, and DPN [odds ratios (ORs): 2.447, 2.267, 2.252; 95% confidence interval: 1.648–3.633, 1.406–3.655, 1.472–3.445]. In the age (years) > 55, T2DM duration (years) > 10, and HbA1c (%) ≥ 7 groups, the risk of high TGs was higher for DKD (ORs: 2.193, 2.419, 2.082), DR (ORs: 2.069, 2.317, 1.993), and DPN (ORs: 1.811, 1.405, 1.427). Conclusion High TG levels increase the risk of diabetic microvascular complications, and patients with older age, longer T2DM duration, and higher HbA1c levels are recommended to keep lipid levels more strictly
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