Research on Image Super-Resolution Reconstruction Mechanism based on Convolutional Neural Network

Large language models(LLMs) have shown its outperforming ability on various tasks and question answering. However, LLMs require high computation cost and large memory cost. At the same time, LLMs may cause privacy leakage when training or prediction procedure contains sensitive information. In this paper, we propose SPA(Side Plugin Adaption), a lightweight architecture for fast on-devices inference and privacy retaining on the constraints of strict on-devices computation and memory constraints. Compared with other on-devices seq2seq generation, SPA could make a fast and stable inference on low-resource constraints, allowing it to obtain cost effiency. Our method establish an interaction between a pretrained LLMs on-cloud and additive parameters on-devices, which could provide the knowledge on both pretrained LLMs and private personal feature.Further more, SPA provides a framework to keep feature-base parameters on private guaranteed but low computational devices while leave the parameters containing general information on the high computational devices.

SPA: Towards A Computational Friendly Cloud-Base and On-Devices Collaboration Seq2seq Personalized Generation

Bayesian Neural Networks (BNNs) have become one of the promising approaches for uncertainty estimation due to the solid theorical foundations. However, the performance of BNNs is affected by the ability of catching uncertainty. Instead of only seeking the distribution of neural network weights by in-distribution (ID) data, in this paper, we propose a new Bayesian Neural Network with an Attached structure (ABNN) to catch more uncertainty from out-of-distribution (OOD) data. We first construct a mathematical description for the uncertainty of OOD data according to the prior distribution, and then develop an attached Bayesian structure to integrate the uncertainty of OOD data into the backbone network. ABNN is composed of an expectation module and several distribution modules. The expectation module is a backbone deep network which focuses on the original task, and the distribution modules are mini Bayesian structures which serve as attachments of the backbone. In particular, the distribution modules aim at extracting the uncertainty from both ID and OOD data. We further provide theoretical analysis for the convergence of ABNN, and experimentally validate its superiority by comparing with some state-of-the-art uncertainty estimation methods Code will be made available.

pdf/be-bayesian-by-attachments-to-catch-more-uncertainty-2zgz6vq782.pdf

Be Bayesian by Attachments to Catch More Uncertainty

SPA: Towards A Computational Friendly Cloud-Base and On-Devices Collaboration Seq2seq Personalized Generation with Causal Inference

Lung cancer is a major cause of cancer-related deaths, and early diagnosis and treatment are crucial for improving patients' survival outcomes. In this paper, we propose to employ convolutional neural networks to model the non-linear relationship between the risk of lung cancer and the lungs' morphology revealed in the CT images. We apply a mini-batched loss that extends the Cox proportional hazards model to handle the non-convexity induced by neural networks, which also enables the training of large data sets. Additionally, we propose to combine mini-batched loss and binary cross-entropy to predict both lung cancer occurrence and the risk of mortality. Simulation results demonstrate the effectiveness of both the mini-batched loss with and without the censoring mechanism, as well as its combination with binary cross-entropy. We evaluate our approach on the National Lung Screening Trial data set with several 3D convolutional neural network architectures, achieving high AUC and C-index scores for lung cancer classification and survival prediction. These results, obtained from simulations and real data experiments, highlight the potential of our approach to improving the diagnosis and treatment of lung cancer. 

pdf/improving-lung-cancer-diagnosis-and-survival-prediction-with-1wb97dcoyoek.pdf

Improving Lung Cancer Diagnosis and Survival Prediction with Deep
  Learning and CT Imaging

Imagine a network, like a socialnetwork or a system of connected devices, is being observed over time. Each node in this network has certain measurements attached to it that can change, like the temperature of a device. Although the overall structure of the network remains constant, these measurements can vary, leading to a complex multivariate time series dataset that exhibits non-stationary characteristics over time. This paper applies a piecewise stationary network vector autoregressive (NAR) model to analyze these network data. The main idea is to partition the entire dataset into segments where the NAR model for each segment remains stationary. The identification of these segments, along with the determination of the NAR processes' autoregressive lag orders, are treated as unknowns. The minimum description length (MDL) principle is employed to develop a criterion for model selection that estimates these unknown parameters. A two-stage genetic algorithm is then formulated to tackle this optimization challenge. The MDL criterion is proven to be consistent in identifying the number and positions of the breakpoints - the junctures where adjacent NAR segments intersect. The effectiveness of the proposed method is demonstrated through simulation studies and real data analysis.

Structural Break Detection in Non-Stationary Network Vector Autoregression Models

Line spectral estimation is an important problem that finds many useful applications in signal processing. Many high-performance methods have been proposed for solving this problem: they select the number of spectral lines and provide point estimates of the frequencies and amplitudes of such spectral lines. This paper studies the line spectral estimation problem from a different and equally important angle: uncertainty quantification. More precisely, this paper develops a novel method that provides an uncertainty measure for the number of spectral lines and also offers point estimates and confidence intervals for other parameters of interest. The proposed method is based on the generalized fiducial inference framework and is shown to possess desirable theoretical and empirical properties. It has also been numerically compared with existing methods in the literature and applied for the detection of exoplanets.

Uncertainty Quantification for Sparse Estimation of Spectral Lines

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Structural Break Detection in Non-Stationary Network Vector Autoregression Models

Uncertainty Quantification for Sparse Estimation of Spectral Lines