Efficient Generalization Improvement Guided by Random Weight Perturbation

To fully uncover the great potential of deep neural networks (DNNs), various learning algorithms have been developed to improve the model's generalization ability. Recently, sharpness-aware minimization (SAM) establishes a generic scheme for generalization improvements by minimizing the sharpness measure within a small neighborhood and achieves state-of-the-art performance. However, SAM requires two consecutive gradient evaluations for solving the min-max problem and inevitably doubles the training time. In this paper, we resort to filter-wise random weight perturbations (RWP) to decouple the nested gradients in SAM. Different from the small adversarial perturbations in SAM, RWP is softer and allows a much larger magnitude of perturbations. Specifically, we jointly optimize the loss function with random perturbations and the original loss function: the former guides the network towards a wider flat region while the latter helps recover the necessary local information. These two loss terms are complementary to each other and mutually independent. Hence, the corresponding gradients can be efficiently computed in parallel, enabling nearly the same training speed as regular training. As a result, we achieve very competitive performance on CIFAR and remarkably better performance on ImageNet (e.g. $\mathbf{ +1.1\%}$) compared with SAM, but always require half of the training time. The code is released at https://github.com/nblt/RWP

Novel Sb−SnO2 Electrode with Ti3+ Self-Doped Urchin-Like Rutile TiO2 Nanoclusters as the Interlayer for the Effective Degradation of Dye Pollutants

Stable and efficient SnO2 electrodes are very promising for effectively degrading refractory organic pollutants in wastewater treatment. In this regard, we firstly prepared Ti3+ self-doped urchin-like rutile TiO2 nanoclusters (TiO2-xNCs) on a Ti mesh substrate by hydrothermal and electroreduction to serve as an interlayer for the deposition of Sb−SnO2. The TiO2-xNCs/Sb−SnO2 anode exhibited a high oxygen evolution potential (2.63 V vs. SCE) and strong ⋅OH generation ability for the enhanced amount of absorbed oxygen species. Thus, the degradation results demonstrated its good rhodamine B (RhB), methylene blue (MB), alizarin yellow R (AYR), and methyl orange (MO) removal performance, with the rate constant increased 5.0, 1.9, 1.9, and 4.7 times, respectively, compared to the control Sb−SnO2 electrode. RhB and AYR degradation mechanisms are also proposed based on the results of high-performance liquid chromatography coupled with mass spectrometry and quenching experiments. More importantly, this unique rutile interlayer prolonged the anode lifetime sixfold, given its good lattice match with SnO2 and the three-dimensional concave–convex structure. Consequently, this work paves a new way for designing the crystal form and structure of the interlayers to obtain efficient and stable SnO2 electrodes for addressing dye wastewater problems

Integrating clinical and cross-cohort metagenomic features: a stable and non-invasive colorectal cancer and adenoma diagnostic model

Background: Dysbiosis is associated with colorectal cancer (CRC) and adenomas (CRA). However, the robustness of diagnostic models based on microbial signatures in multiple cohorts remains unsatisfactory.Materials and Methods: In this study, we used machine learning models to screen metagenomic signatures from the respective cross-cohort datasets of CRC and CRA (selected from CuratedMetagenomicData, each disease included 4 datasets). Then select a CRC and CRA data set from the CuratedMetagenomicData database and meet the requirements of having both metagenomic data and clinical data. This data set will be used to verify the inference that integrating clinical features can improve the performance of microbial disease prediction models.Results: After repeated verification, we selected 20 metagenomic features that performed well and were stably expressed within cross-cohorts to represent the diagnostic role of bacterial communities in CRC/CRA. The performance of the selected cross-cohort metagenomic features was stable for multi-regional and multi-ethnic populations (CRC, AUC: 0.817–0.867; CRA, AUC: 0.766–0.833). After clinical feature combination, AUC of our integrated CRC diagnostic model reached 0.939 (95% CI: 0.932–0.947, NRI=30%), and that of the CRA integrated model reached 0.925 (95%CI: 0.917–0.935, NRI=18%).Conclusion: In conclusion, the integrated model performed significantly better than single microbiome or clinical feature models in all cohorts. Integrating cross-cohort common discriminative microbial features with clinical features could help construct stable diagnostic models for early non-invasive screening for CRC and CRA

A novel approach to optimising well trajectory in heterogeneous reservoirs based on the fast-marching method

To achieve efficient recovery of subsurface energy resources, a suitable trajectory needs to be identified for the production well. In this study, a new approach is presented for automated identification of optimum well trajectories in heterogeneous oil/gas reservoirs. The optimisation procedures are as follows. First, a productivity potential map is generated based on the site characterisation data of a reservoir (when available). Second, based on the fast-marching method, well paths are generated from a number of entrance positions to a number of exit points at opposite sides of the reservoir. The well trajectory is also locally constrained by a prescribed maximum curvature to ensure that the well trajectory is drillable. Finally, the optimum well trajectory is selected from all the candidate paths based on the calculation of a benefit-to-cost ratio. If required, a straight directional well path, may also be derived through a linear approximation to the optimised non-linear trajectory by least squares analysis. Model performance has been demonstrated in both 2D and 3D. In the 2D example, the benefit-to-cost ratio of the optimised well is much higher than that of a straight well; in the 3D example, laterals of various curvatures are generated. The applicability of the method is tested by exploring different reservoir heterogeneities and curvature constraints. This approach can be applied to determine the entrance/exit positions and the well path for subsurface energy system development, which is useful for field applications

Influence Pathway Discovery on Social Media

This paper addresses influence pathway discovery, a key emerging problem in today's online media. We propose a discovery algorithm that leverages recently published work on unsupervised interpretable ideological embedding, a mapping of ideological beliefs (done in a self-supervised fashion) into interpretable low-dimensional spaces. Computing the ideological embedding at scale allows one to analyze correlations between the ideological positions of leaders, influencers, news portals, or population segments, deriving potential influence pathways. The work is motivated by the importance of social media as the preeminent means for global interactions and collaborations on today's Internet, as well as their frequent (mis-)use to wield influence that targets social beliefs and attitudes of selected populations. Tools that enable the understanding and mapping of influence propagation through population segments on social media are therefore increasingly important. In this paper, influence is measured by the perceived ideological shift over time that is correlated with influencers' activity. Correlated shifts in ideological embeddings indicate changes, such as swings/switching (among competing ideologies), polarization (depletion of neutral ideological positions), escalation/radicalization (shifts to more extreme versions of the ideology), or unification/cooldown (shifts towards more neutral stances). Case-studies are presented to explore selected influence pathways (i) in a recent French election, (ii) during political discussions in the Philippines, and (iii) for some Russian messaging during the Russia/Ukraine conflict.Comment: This paper is accepted by IEEE CIC as an invited vision pape

Si-Ni-San alleviates early life stress-induced depression-like behaviors in adolescence via modulating Rac1 activity and associated spine plasticity in the nucleus accumbens

Background: Early life stress (ELS) is a major risk factor for depression in adolescents. The nucleus accumbens (NAc) is a key center of the reward system, and spine remodeling in the NAc contributes to the development of depression. The Si-Ni-San formula (SNS) is a fundamental prescription for treating depression in traditional Chinese medicine. However, little is known about the effects of SNS on behavioral abnormalities and spine plasticity in the NAc induced by ELS.Purpose: This study aimed to investigate the therapeutic effect and the modulatory mechanism of SNS on abnormal behaviors and spine plasticity in the NAc caused by ELS.Methods: We utilized a model of ELS that involved maternal separation with early weaning to explore the protective effects of SNS on adolescent depression. Depressive-like behaviors were evaluated by the sucrose preference test, the tail suspension test, and the forced swimming test; anxiety-like behaviors were monitored by the open field test and the elevated plus maze. A laser scanning confocal microscope was used to analyze dendritic spine remodeling in the NAc. The activity of Rac1 was detected by pull-down and Western blot tests. Viral-mediated gene transfer of Rac1 was used to investigate its role in ELS-induced depression-like behaviors in adolescence.Results: ELS induced depression-like behaviors but not anxiety-like behaviors in adolescent mice, accompanied by an increase in stubby spine density, a decrease in mushroom spine density, and decreased Rac1 activity in the NAc. Overexpression of constitutively active Rac1 in the NAc reversed depression-related behaviors, leading to a decrease in stubby spine density and an increase in mushroom spine density. Moreover, SNS attenuated depression-like behavior in adolescent mice and counteracted the spine abnormalities in the NAc induced by ELS. Additionally, SNS increased NAc Rac1 activity, and the inhibition of Rac1 activity weakened the antidepressant effect of SNS.Conclusion: These results suggest that SNS may exert its antidepressant effects by modulating Rac1 activity and associated spine plasticity in the NAc

CdSe Quantum Dot (QD)-Induced Morphological and Functional Impairments to Liver in Mice

Quantum dots (QDs), as unique nanoparticle probes, have been used in in vivo fluorescence imaging such as cancers. Due to the novel characteristics in fluorescence, QDs represent a family of promising substances to be used in experimental and clinical imaging. Thus far, the toxicity and harmful health effects from exposure (including environmental exposure) to QDs are not recognized, but are largely concerned by the public. To assess the biological effects of QDs, we established a mouse model of acute and chronic exposure to QDs. Results from the present study suggested that QD particles could readily spread into various organs, and liver was the major organ for QD accumulation in mice from both the acute and chronic exposure. QDs caused significant impairments to livers from mice with both acute and chronic QD exposure as reflected by morphological alternation to the hepatic lobules and increased oxidative stress. Moreover, QDs remarkably induced the production of intracellular reactive oxygen species (ROS) along with cytotoxicity, as characterized by a significant increase of the malondialdehyde (MDA) level within hepatocytes. However, the increase of the MDA level in response to QD treatment could be partially blunted by the pre-treatment of cells with beta-mercaptoethanol (β-ME). These data suggested ROS played a crucial role in causing oxidative stress-associated cellular damage from QD exposure; nevertheless other unidentified mediators might also be involved in QD-mediated cellular impairments. Importantly, we demonstrated that the hepatoxicity caused by QDs in vivo and in vitro was much greater than that induced by cadmium ions at a similar or even a higher dose. Taken together, the mechanism underlying QD-mediated biological influences might derive from the toxicity of QD particles themselves, and from free cadmium ions liberated from QDs as well

Pozzolanic activity of FCC catalyst waste slag (CWS) for cement and geopolymer production

Catalyst waste slag (CWS) is generated in large amounts when fabricating the catalyst required in the fluid cracking catalyst (FCC) process used for oil refining. Currently, CWS is landfilled. This paper characterizes the CWS and measures its pozzolanic activity. It determines whether the CWS can be used as partial Portland cement (PC) replacement for the first time in the literature. CWS is primarily composed of Al2O3 and CaO. It contains a negligible quantity of heavy metals that can be immobilised in a matrix. The raw CWS is totally amorphous and extremely reactive which results in flash set. Calcination is an effective method to control the reactivity of CWS. Reactivity increased when CWS was calcined between 300 and 600 °C and it peaked at 500 °C, but this caused flash set. Calcination at 800 °C lowers reactivity (as the highly disordered aluminium phases achieve a greater order) which allows for proper handling. Temperatures over 800 °C caused partial crystallization significantly lowering reactivity.The 800°C-calcined CWS reached high mechanical index (6.25), comparable to other pozzolans such as fly ash and red mud, and greater than alum sludge. CWS combines lime profusely and develops more abundant cementing hydrates than similar pozzolans such as calcined alum sludge. The pozzolanic reaction of the 800°C-calcined CWS provides abundant cementing minerals including AFt and AFm phases, calcium aluminium carbonate hydrates and calcium aluminium hydrates.The high reactivity of CWS and its prolific production of cementing phases in pozzolanic reactions indicate that it can be used as a supplementary cementitious material in PC and lime systems; and that it can be used as a precursor to produce low-carbon and geopolymer cements. CWS constitutes a reactive aluminium source which, in a PC system, participates in hydration reactions and can enhance the properties of the resultant materials