XGrad: Boosting Gradient-Based Optimizers With Weight Prediction

In this paper, we propose a general deep learning training framework XGrad which introduces weight prediction into the popular gradient-based optimizers to boost their convergence and generalization when training the deep neural network (DNN) models. In particular, ahead of each mini-batch training, the future weights are predicted according to the update rule of the used optimizer and are then applied to both the forward pass and backward propagation. In this way, during the whole training period, the optimizer always utilizes the gradients w.r.t. the future weights to update the DNN parameters, making the gradient-based optimizer achieve better convergence and generalization compared to the original optimizer without weight prediction. XGrad is rather straightforward to implement yet pretty effective in boosting the convergence of gradient-based optimizers and the accuracy of DNN models. Empirical results concerning the most three popular gradient-based optimizers including SGD with momentum, Adam, and AdamW demonstrate the effectiveness of our proposal. The experimental results validate that XGrad can attain higher model accuracy than the original optimizers when training the DNN models. The code of XGrad will be available at: https://github.com/guanleics/XGrad.Comment: arXiv admin note: text overlap with arXiv:2302.0019

The use of Xuanbai Chengqi decoction on monkeypox disease through the estrone-target AR interaction

IntroductionAfter COVID-19, there was an outbreak of a new infectious disease caused by monkeypox virus. So far, no specific drug has been found to treat it. Xuanbai Chengqi decoction (XBCQD) has shown effects against a variety of viruses in China.MethodsWe searched for the active compounds and potential targets for XBCQD from multiple open databases and literature. Monkeypox related targets were searched out from the OMIM and GeneCards databases. After determining the assumed targets of XBCQD for monkeypox treatment, we built the PPI network and used R for GO enrichment and KEGG pathway analysis. The interactions between the active compounds and the hub targets were investigated by molecular docking and molecular dynamics (MD) simulations.ResultsIn total, 5 active compounds and 10 hub targets of XBCQD were screened out. GO enrichment and KEGG analysis demonstrated that XBCQD plays a therapeutic role in monkeypox mainly by regulating signaling pathways related to viral infection and inflammatory response. The main active compound estrone binding to target AR was confirmed to be the best therapy choice for monkeypox.DiscussionThis study systematically explored the interactions between the bioactive compounds of XBCQD and the monkeypox-specific XBCQD targets using network pharmacological methods, bioinformatics analyses and molecular simulations, suggesting that XBCQD could have a beneficial therapeutic effect on monkeypox by reducing the inflammatory damage and viral replication via multiple pathways. The use of XBCQD on monkeypox disease was confirmed to be best worked through the estrone-target AR interaction. Our work could provide evidence and guidance for further research on the treatment of monkeypox disease

GROVE: A Retrieval-augmented Complex Story Generation Framework with A Forest of Evidence

Conditional story generation is significant in human-machine interaction, particularly in producing stories with complex plots. While Large language models (LLMs) perform well on multiple NLP tasks, including story generation, it is challenging to generate stories with both complex and creative plots. Existing methods often rely on detailed prompts to guide LLMs to meet target conditions, which inadvertently restrict the creative potential of the generated stories. We argue that leveraging information from exemplary human-written stories facilitates generating more diverse plotlines. Delving deeper into story details helps build complex and credible plots. In this paper, we propose a retrieval-au\textbf{G}mented sto\textbf{R}y generation framework with a f\textbf{O}rest of e\textbf{V}id\textbf{E}nce (GROVE) to enhance stories' complexity. We build a retrieval repository for target conditions to produce few-shot examples to prompt LLMs. Additionally, we design an ``asking-why'' prompting scheme that extracts a forest of evidence, providing compensation for the ambiguities that may occur in the generated story. This iterative process uncovers underlying story backgrounds. Finally, we select the most fitting chains of evidence from the evidence forest and integrate them into the generated story, thereby enhancing the narrative's complexity and credibility. Experimental results and numerous examples verify the effectiveness of our method.Comment: Findings of EMNLP 202

Periplasmic biomineralization for semi-artificial photosynthesis

Semiconductor-based biointerfaces are typically established either on the surface of the plasma membrane or within the cytoplasm. In Gram-negative bacteria, the periplasmic space, characterized by its confinement and the presence of numerous enzymes and peptidoglycans, offers additional opportunities for biomineralization, allowing for nongenetic modulation interfaces. We demonstrate semiconductor nanocluster precipitation containing single- and multiple-metal elements within the periplasm, as observed through various electron- and x-ray-based imaging techniques. The periplasmic semiconductors are metastable and display defect-dominant fluorescent properties. Unexpectedly, the defect-rich (i.e., the low-grade) semiconductor nanoclusters produced in situ can still increase adenosine triphosphate levels and malate production when coupled with photosensitization. We expand the sustainability levels of the biohybrid system to include reducing heavy metals at the primary level, building living bioreactors at the secondary level, and creating semi-artificial photosynthesis at the tertiary level. The biomineralization-enabled periplasmic biohybrids have the potential to serve as defect-tolerant platforms for diverse sustainable applications

An interlaboratory comparison of aerosol inorganic ion measurements by ion chromatography : Implications for aerosol pH estimate

Water-soluble inorganic ions such as ammonium, nitrate and sulfate are major components of fine aerosols in the atmosphere and are widely used in the estimation of aerosol acidity. However, different experimental practices and instrumentation may lead to uncertainties in ion concentrations. Here, an intercomparison experiment was conducted in 10 different laboratories (labs) to investigate the consistency of inorganic ion concentrations and resultant aerosol acidity estimates using the same set of aerosol filter samples. The results mostly exhibited good agreement for major ions Cl-, SO2-4, NO-3, NHC4 and KC. However, F-, Mg2C and Ca2C were observed with more variations across the different labs. The Aerosol Chemical Speciation Monitor (ACSM) data of nonrefractory SO2-4, NO-3 and NHC4 generally correlated very well with the filter-analysis-based data in our study, but the absolute concentrations differ by up to 42 %. Cl-from the two methods are correlated, but the concentration differ by more than a factor of 3. The analyses of certified reference materials (CRMs) generally showed a good detection accuracy (DA) of all ions in all the labs, the majority of which ranged between 90 % and 110 %. The DA was also used to correct the ion concentrations to showcase the importance of using CRMs for calibration check and quality control. Better agreements were found for Cl-, SO2-4, NO-3, NHC4 and KC across the labs after their concentrations were corrected with DA; the coefficient of variation (CV) of Cl-, SO2-4, NO-3, NHC4 and KC decreased by 1.7 %, 3.4 %, 3.4 %, 1.2 % and 2.6 %, respectively, after DA correction. We found that the ratio of anion to cation equivalent concentrations (AE/CE) and ion balance (anions-cations) are not good indicators for aerosol acidity estimates, as the results in different labs did not agree well with each other. In situ aerosol pH calculated from the ISORROPIA II thermodynamic equilibrium model with measured ion and ammonia concentrations showed a similar trend and good agreement across the 10 labs. Our results indicate that although there are important uncertainties in aerosol ion concentration measurements, the estimated aerosol pH from the ISORROPIA II model is more consistent

Changes in the Structure and Digestibility of Wrinkled Pea Starch with Malic Acid Treatment

Resistant starch has gradually become a popular food component due to its beneficial physiological effects and heat resistance during processing. In this study, the structure, reaction mechanism, and digestibility of wrinkled pea starch with malic acid and heat⁻moisture treatment (HMT) are investigated. The degree of substitution (DS) of malate starch, HMT-malate starch, and malate-HMT starch was 0.164, 0.280, and 0.146, respectively. Malate starch remained in its complete particle form and pronounced birefringence was displayed. However, the malate-HMT starch sample was almost completely broken into pieces and lost the polarized cross. All modified starch samples had a decreased swelling power and a new peak at 1731⁻1741 cm−1 shown by FTIR. From the 13C CP/MAS NMR (Cross Polarizatio/Magic Angle Spinning Nuclear Magnetic Resonance) spectra, all the modified starches had extra peaks at 38.5 ppm and 172.8 ppm. After esterification treatment, the resistant starch (RS) and slowly digestible starch (SDS) content of starch samples increased dramatically. The higher content of RS and lower enzymatic hydrolysis rate of the malate starch could be used to produce low-calorie foods and have potential health benefits

Extended PGA for Spotlight SAR-Filtered Backprojection Imagery

The phase gradient autofocus (PGA) is a robust autofocusing approach that can efficiently refocus defocused synthetic aperture radar (SAR) imagery produced by frequency-domain algorithms. However, from a conventional viewpoint, PGA cannot be extended to refocus SAR imagery produced by time-domain algorithms, such as the filtered backprojection (FBP), as the spectrum of the FBP imagery is range ambiguous and azimuth space-variant. In this letter, a novel interpretation of FBP is presented, in which the spectrum structure of the FBP imagery is analyzed in detail. By incorporating the derived spectral information, an efficient spectrum preprocessing is proposed for spectrum restructuring. After this preprocessing, PGA is shown to be able to refocus defocused FBP imagery. The validity and feasibility of the proposed autofocusing approach are demonstrated using both simulated and experimental data

Study on the Decoupled Charge Effect in Deep-Hole Cumulative Blasting of Coal Seam

Five models of cumulative blasting are established by using ANSYS/LS-DYNA to study the effect of decoupling coefficient on cumulative blasting to improve coal seam permeability. The formation and migration process of the shaped energy jets with two kinds of decoupling coefficient are compared and analyzed; also, the propagation of explosive stress waves is represented. The result showed that the air in the blast hole is the key to the formation and migration of the condensing jet. The air in the hole also could reduce the attenuation of stress wave in a certain range. However, if the decoupling coefficient is too large, the air in the hole will consume excessive explosive energy, which is also not conducive to energy transfer. Therefore, there is an optimum decoupling coefficient which can minimize the coal crushing area, increase the coal fissure area, and improve the gas extraction rate. Besides, the cumulative blasting tests were carried out in a coal seam. The test results show that decoupling charge could effectively improve coal seam permeability, and the blasting effect was better when the decoupling coefficient is between 1.67 and 2

Effect of an Atmospheric Pressure Plasma Jet on the Structure and Physicochemical Properties of Waxy and Normal Maize Starch

In present study, a novel physical modification of waxy maize starch (WMS) and normal maize starch (NMS) was investigated by using an atmospheric pressure plasma jet (APPJ) treatment. The effect on the structure and physicochemical properties of both starches was demonstrated by treatment with a 5% starch suspension (w/w) with APPJ for short periods of time (1, 3, 5, or 7 min). The pH of WMS and NMS was decreased after APPJ treatment from 5.42 to 4.94, and 5.09 to 4.75, respectively. The water-binding capacity (WBC) (WMS: 105.19%⁻131.27%, NMS: 83.56%⁻95.61%) and swelling volume (SV) (WMS: 2.96 g/mL⁻3.33 g/mL, NMS: 2.75 g/mL⁻3.05 g/mL) of the starches were obviously increased by APPJ treatment. The surfaces of starch granules were wrecked, due to plasma etching. No changes in the crystalline types of both starches were observed. However, the relative crystallinities (RCs) of WMS and NMS were reduced from 46.7% to 42.0%, and 40.1% to 35.7%, respectively. Moreover, the short-range molecular orders of both starches were slightly reduced. In addition, APPJ treatment resulted in lower gelatinization temperature and enthalpies. Therefore, APPJ provides a mild and green approach to starch modification, showing great potential for applications in the food and non-food industry