Enhancing Low-Precision Sampling via Stochastic Gradient Hamiltonian Monte Carlo

Low-precision training has emerged as a promising low-cost technique to enhance the training efficiency of deep neural networks without sacrificing much accuracy. Its Bayesian counterpart can further provide uncertainty quantification and improved generalization accuracy. This paper investigates low-precision sampling via Stochastic Gradient Hamiltonian Monte Carlo (SGHMC) with low-precision and full-precision gradient accumulators for both strongly log-concave and non-log-concave distributions. Theoretically, our results show that, to achieve $\epsilon$-error in the 2-Wasserstein distance for non-log-concave distributions, low-precision SGHMC achieves quadratic improvement ($\widetilde{\mathbf{O}}\left({\epsilon^{-2}{\mu^*}^{-2}\log^2\left({\epsilon^{-1}}\right)}\right)$) compared to the state-of-the-art low-precision sampler, Stochastic Gradient Langevin Dynamics (SGLD) ($\widetilde{\mathbf{O}}\left({{\epsilon}^{-4}{\lambda^{*}}^{-1}\log^5\left({\epsilon^{-1}}\right)}\right)$). Moreover, we prove that low-precision SGHMC is more robust to the quantization error compared to low-precision SGLD due to the robustness of the momentum-based update w.r.t. gradient noise. Empirically, we conduct experiments on synthetic data, and {MNIST, CIFAR-10 \& CIFAR-100} datasets, which validate our theoretical findings. Our study highlights the potential of low-precision SGHMC as an efficient and accurate sampling method for large-scale and resource-limited machine learning

Research on real-time scheduling algorithm of federated learning tasks based on energy optimization on NoC platform

Federal learning technology can realize global data sharing and reduce the risk of privacy disclosure under the premise of ensuring data security. Aiming at the problem of task assignment scheduling in federated learning process, this paper studies the problem of federated learning task scheduling on NoC multi-core platform under cloud computing architecture. Considering the limited computing resources of physical nodes, this paper describes the problem of optimal assignment and execution of tasks on network nodes as a mixed integer nonlinear programming problem. In order to improve the computational effi ciency, the original problem can be equitably converted into a mixed integer linear programming problem. Finally, the scheduling method is verifi ed by real application of task set, and the infl uence of parameter selection on scheduling scheme is studied

Problems and Suggestions of Peasants’ Property Income in Jilin Province

Peasant revenue increment has long been an issue of public concern and is widely emphasized since the 16th National Congress of CPC. In the state that it is difficult to increase peasants’ salary net income, family operation net income and metastatic net income in Jilin, there is room to develop to increase their property net income. In Jilin, the defects and deficiencies in rural land property right system, the financial supply and the social security system have limited the increase of peasants’ property income. To take measures and to expedite the development of appropriate institutional arrangements will not only help increase the peasants’ property income in Jilin, but also help increase the peasant net income and promote the prosperity of the rural economy

New Progress of Epigenetic Biomarkers in Urological Cancer

Urological cancers consist of bladder, kidney, prostate, and testis cancers and they are generally silenced at their early stage, which leads to the loss of the best opportunity for early diagnosis and treatment. Desired biomarkers are scarce for urological cancers and current biomarkers are lack of specificity and sensitivity. Epigenetic alterations are characteristic of nearly all kinds of human malignances including DNA methylation, histone modification, and miRNA regulation. Besides, the detection of these epigenetic conditions is easily accessible especially for urine, best target for monitoring the diseases of urinary system. Here, we summarize some new progress about epigenetic biomarkers in urological cancers, hoping to provide new thoughts for the diagnosis, treatment, and prognosis of urological cancers

Key Information Retrieval to Classify the Unstructured Data Content of Preferential Trade Agreements

With the rapid proliferation of textual data, predicting long texts has emerged as a significant challenge in the domain of natural language processing. Traditional text prediction methods encounter substantial difficulties when grappling with long texts, primarily due to the presence of redundant and irrelevant information, which impedes the model's capacity to capture pivotal insights from the text. To address this issue, we introduce a novel approach to long-text classification and prediction. Initially, we employ embedding techniques to condense the long texts, aiming to diminish the redundancy therein. Subsequently,the Bidirectional Encoder Representations from Transformers (BERT) embedding method is utilized for text classification training. Experimental outcomes indicate that our method realizes considerable performance enhancements in classifying long texts of Preferential Trade Agreements. Furthermore, the condensation of text through embedding methods not only augments prediction accuracy but also substantially reduces computational complexity. Overall, this paper presents a strategy for long-text prediction, offering a valuable reference for researchers and engineers in the natural language processing sphere.Comment: AI4TS Workshop@AAAI 2024 accepted publicatio

Comparison of 18F-FDG and 68Ga-FAPI PET/CT in the diagnosis of lung metastasis in different malignant tumors

Background and purpose: 18F-flurodeoxyglucose (18F-FDG) positron emission tomography and computed tomography (PET/CT) is a common method for the diagnosis of malignant tumors with distant metastases. However the detection of lung metastases, especially small lesions, is still unsatisfactory. 68Ga-labeled fibroblast activation protein inhibitors (68Ga-FAPI) have been used to target fibroblast activating proteins and visualize tumor stroma. The diagnostic value of 68Ga-FAPI PET/CT is higher than that of 18F-FDG in the primary sites and metastases of most cancers, but no comparative study has been seen in lung metastases of malignant tumors. Therefore, this study aimed to investigate the diagnostic value of 18F-FDG and 68Ga-FAPI PET/CT in different malignant lung metastases. Methods: The clinical, pathological and imaging data of 20 patients with lung metastasis who underwent 18F-FDG and 68Ga-FAPI PET/CT examination within one week in Fudan University Shanghai Cancer Center from May 2020 to March 2022 were retrospectively analyzed. Among them, 11 cases were epithelial malignant tumors (carcinoma), and 9 cases were mesophyll malignant tumors (sarcoma). The semi-quantitative metabolic parameters including maximum standard uptake value (SUVmax) and target-to-background ratio (TBR) of 68Ga-FAPI and 18F-FDG were compared by paired t test. The linear correlation between SUVmax and TBR and the short diameter of lung metastasis were analyzed. Results: A total of 81 lung metastases (51 carcinomas and 30 sarcomas) were detected in 20 patients, 72 positive lesions were detected by 18F-FDG and 70 positive lesions by 68Ga-FAPI. Compared with 68Ga-FAPI, 18F-FDG uptake was higher in lung metastases, especially those of carcinoma (P<0.001). The results of linear correlation analysis showed that the semi-quantitative metabolic parameters of the two imaging probes were positively correlated with the short diameter of lung metastases (P<0.001). Conclusion: 68Ga-FAPI has no obvious advantage in the detection of lung metastases from malignant tumors. Especially in the diagnosis of lung metastases from epithelial tissues, the uptake of 18F-FDG tends to be higher

TextDiff: Mask-Guided Residual Diffusion Models for Scene Text Image Super-Resolution

The goal of scene text image super-resolution is to reconstruct high-resolution text-line images from unrecognizable low-resolution inputs. The existing methods relying on the optimization of pixel-level loss tend to yield text edges that exhibit a notable degree of blurring, thereby exerting a substantial impact on both the readability and recognizability of the text. To address these issues, we propose TextDiff, the first diffusion-based framework tailored for scene text image super-resolution. It contains two modules: the Text Enhancement Module (TEM) and the Mask-Guided Residual Diffusion Module (MRD). The TEM generates an initial deblurred text image and a mask that encodes the spatial location of the text. The MRD is responsible for effectively sharpening the text edge by modeling the residuals between the ground-truth images and the initial deblurred images. Extensive experiments demonstrate that our TextDiff achieves state-of-the-art (SOTA) performance on public benchmark datasets and can improve the readability of scene text images. Moreover, our proposed MRD module is plug-and-play that effectively sharpens the text edges produced by SOTA methods. This enhancement not only improves the readability and recognizability of the results generated by SOTA methods but also does not require any additional joint training. Available Codes:https://github.com/Lenubolim/TextDiff

Influence of area-to-volume ratios on dissolution characteristics and mechanical properties of acid-corroded sandstone

To study the effect of area-to-volume ratio on the dissolution and deterioration characteristics of sandstone in the static acid-rock reaction system, the HCl and H2SO4 solutions with pH=2 and 5 are selected as corrosion environments, and the different area-to-volume ratios are set by changing surface areas of sandstone. The effects of area-to-volume ratios on the physicochemical and mechanical properties of sandstone are studied. According to the acid-rock reaction theory, the effect of the area-to-volume ratio on the diffusion-dissolution mechanism during sandstone corrosion is analyzed. The results show that the sandstone mass loss rate and amount of substance of total cations are all related to the corrosion time as a power function. The area-to-volume is positively correlated with the dissolution rate constant and has little effect on the reaction order. The reaction order is less than one in different environments, indicating that the sandstone corrosion rate decreases gradually with soaking time. In the pH=2、5 HCl solution and pH=2 H2SO4 solution, the amount of substance of cation shows N(Ca2+) > N(Na+) > N(Mg2+) > N(K+), and in the pH=5 H2SO4 solution, it is N(Na+) > N(Ca2+) > N(Mg2+) ≈N(K+). The acid-rock reaction can be summarized as two mechanisms: diffusion control and chemical reaction control. The two control parameters are negatively correlated with the area-to-volume ratio and positively with the pH value of solutions. The parameter values in the H2SO4 solutions are slightly larger than the corresponding values in the HCl solutions. The interaction between sandstone and acid in different conditions is dominated by the chemical reaction. The area-to-volume ratio significantly influences diffusion more than the chemical reaction. The mechanical properties of sandstone are weakened after acid corrosion. The damage of sandstone under uniaxial compression can be divided into four stages: compaction, elastic deformation, plastic yielding and post-peak. The peak strength and elastic modulus decrease, the peak strain increases, the brittleness declines, and the ductility is enhanced. The larger the area-to-volume ratio, the more severe the sandstone deterioration is. Overall, the smaller the pH value of solutions, the more prominent the effects of the area-to-volume ratio on the dissolution characteristics and mechanical properties of sandstone are, which is more obvious in the HCl solutions than in the H2SO4 solutions. The finding can provide theoretical references for the safety assessment and disaster prevention of rock mass engineering under an acidic environment