HAMNER: Headword Amplified Multi-span Distantly Supervised Method for Domain Specific Named Entity Recognition

To tackle Named Entity Recognition (NER) tasks, supervised methods need to obtain sufficient cleanly annotated data, which is labor and time consuming. On the contrary, distantly supervised methods acquire automatically annotated data using dictionaries to alleviate this requirement. Unfortunately, dictionaries hinder the effectiveness of distantly supervised methods for NER due to its limited coverage, especially in specific domains. In this paper, we aim at the limitations of the dictionary usage and mention boundary detection. We generalize the distant supervision by extending the dictionary with headword based non-exact matching. We apply a function to better weight the matched entity mentions. We propose a span-level model, which classifies all the possible spans then infers the selected spans with a proposed dynamic programming algorithm. Experiments on all three benchmark datasets demonstrate that our method outperforms previous state-of-the-art distantly supervised methods.Comment: 9 pages, 2 figure

Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models

Due to the ease of training, ability to scale, and high sample quality, diffusion models (DMs) have become the preferred option for generative modeling, with numerous pre-trained models available for a wide variety of datasets. Containing intricate information about data distributions, pre-trained DMs are valuable assets for downstream applications. In this work, we consider learning from pre-trained DMs and transferring their knowledge to other generative models in a data-free fashion. Specifically, we propose a general framework called Diff-Instruct to instruct the training of arbitrary generative models as long as the generated samples are differentiable with respect to the model parameters. Our proposed Diff-Instruct is built on a rigorous mathematical foundation where the instruction process directly corresponds to minimizing a novel divergence we call Integral Kullback-Leibler (IKL) divergence. IKL is tailored for DMs by calculating the integral of the KL divergence along a diffusion process, which we show to be more robust in comparing distributions with misaligned supports. We also reveal non-trivial connections of our method to existing works such as DreamFusion, and generative adversarial training. To demonstrate the effectiveness and universality of Diff-Instruct, we consider two scenarios: distilling pre-trained diffusion models and refining existing GAN models. The experiments on distilling pre-trained diffusion models show that Diff-Instruct results in state-of-the-art single-step diffusion-based models. The experiments on refining GAN models show that the Diff-Instruct can consistently improve the pre-trained generators of GAN models across various settings

Fatty acid metabolism is related to the immune microenvironment changes of gastric cancer and RGS2 is a new tumor biomarker

BackgroundAlterations in lipid metabolism promote tumor progression. However, the role of lipid metabolism in the occurrence and development of gastric cancer have not been fully clarifiedMethodHere, genes that are related to fatty acid metabolism and differentially-expressed between normal and gastric cancer tissues were identified in the TCGA-STAD cohort. The intersection of identified differentially-expressed genes with Geneset was determined to obtain 78 fatty acid metabolism-related genes. The ConsensusClusterPlus R package was used to perform differentially-expressed genes, which yielded divided two gastric cancer subtypes termed cluster 1 and cluster 2.ResultsPatients in cluster 2 was found to display poorer prognosis than patients in cluster 1. Using machine learning method to select 8 differentially expressed genes among subtypes to construct fatty acid prognostic risk score model (FARS), which was found to display good prognostic efficacy. We also identified that certain anticancer drugs, such as bortezomib, elesclomol, GW843682X, and nilotinib, showed significant sensitivity in the high FARS score group. RGS2 was selected as the core gene upon an analysis of the gastric cancer single-cell, and Western blotting and immunofluorescence staining results revealed high level of expression of this gene in gastric cancer cells. The results of immunohistochemical staining showed that a large amount of RGS2 was deposited in the stroma in gastric cancer. A pan-cancer analysis also revealed a significant association of RGS2 with TMB, TIDE, and CD8+ T-cell infiltration in other cancer types as well. RGS2 may thus be studied further as a new target for immunotherapy in future studies on gastric cancer.ConclusionIn summary, the FARS model developed here enhances our understanding of lipid metabolism in the TME in gastric cancer, and provides a theoretical basis for predicting tumor prognosis and clinical treatment

SA-Solver: Stochastic Adams Solver for Fast Sampling of Diffusion Models

Diffusion Probabilistic Models (DPMs) have achieved considerable success in generation tasks. As sampling from DPMs is equivalent to solving diffusion SDE or ODE which is time-consuming, numerous fast sampling methods built upon improved differential equation solvers are proposed. The majority of such techniques consider solving the diffusion ODE due to its superior efficiency. However, stochastic sampling could offer additional advantages in generating diverse and high-quality data. In this work, we engage in a comprehensive analysis of stochastic sampling from two aspects: variance-controlled diffusion SDE and linear multi-step SDE solver. Based on our analysis, we propose SA-Solver, which is an improved efficient stochastic Adams method for solving diffusion SDE to generate data with high quality. Our experiments show that SA-Solver achieves: 1) improved or comparable performance compared with the existing state-of-the-art sampling methods for few-step sampling; 2) SOTA FID scores on substantial benchmark datasets under a suitable number of function evaluations (NFEs)

Microwave-assisted non-thermal hemp degumming

The microwave-assisted non-thermal degumming of hemp fibre has been studied and then compared with the water bath heating under different time and temperature conditions. The results show that the residual gum content of the lean hemp using microwave-assisted heating method is lower than that obtained using water bath heating. The residual gum content gap between the two degumming processes increases first and then decreases as the heating time and temperature are increased. This proves the existence of non-thermal effects in microwave heating process besides the thermal effects in water bath heating. In addition, the structures of the lean hemp fibres obtained from these two methods are also studied by scanning electron microscopy and fourier transform infrared spectroscopy.

Microstructure and Properties of CoCrFeMnNiMox High-Entropy Alloy Coating by Laser Cladding

45 steel has the problems of low wear resistance and poor corrosion resistance. CoCrFeMnNiMox (x=0.00, 0.25, 0.50, 0.75, 1.00) high-entropy alloy coating was prepared on 45 steel by laser cladding. The influence of Mo on the microstructure and properties of coating were explored in detail. The results show that the CoCrFeMnNiMox high-entropy alloy coating is composed of a single face-centered cubic (FCC)solid-solution phase. The microstructure of the Mo-containing coating is a typical dendritic and interdendritic structure, which is caused by the heterogeneous nucleation of the molten pool during the solidification process. The microhardness of the coating increases with the increase of x, and the maximum microhardness of the Mo1.00 coating is 2.391 GPa. Quantitative calculations show that solution strengthening is the main reason for the increase of microhardness. With the increase of Mo mass fraction, the wear mechanism evolves from adhesive wear to abrasive wear and oxidative wear. The Mo1.00 coating has the lowest volume wear rate (0.68×10-4 mm3/(N·m)). The influence of the passivation process on the corrosion resistance of coating was analyzed based on the point defect model theory. The addition of the Mo element increases the dehydration rate of the passivation behavior of coating, which makes the oxide layer thicker, and thereby improving the corrosion resistance of coating. The corrosion mechanism of coatings is intergranular corrosion. Mo0.75 coating has the smallest self-corrosion current density and the most positive self-corrosion potential, which are 3.69×10-6 A/cm2 and -0.432 V, respectively