Trust Region Methods For Nonconvex Stochastic Optimization Beyond Lipschitz Smoothness

In many important machine learning applications, the standard assumption of having a globally Lipschitz continuous gradient may fail to hold. This paper delves into a more general $(L_0, L_1)$-smoothness setting, which gains particular significance within the realms of deep neural networks and distributionally robust optimization (DRO). We demonstrate the significant advantage of trust region methods for stochastic nonconvex optimization under such generalized smoothness assumption. We show that first-order trust region methods can recover the normalized and clipped stochastic gradient as special cases and then provide a unified analysis to show their convergence to first-order stationary conditions. Motivated by the important application of DRO, we propose a generalized high-order smoothness condition, under which second-order trust region methods can achieve a complexity of $\mathcal{O}(\epsilon^{-3.5})$ for convergence to second-order stationary points. By incorporating variance reduction, the second-order trust region method obtains an even better complexity of $\mathcal{O}(\epsilon^{-3})$, matching the optimal bound for standard smooth optimization. To our best knowledge, this is the first work to show convergence beyond the first-order stationary condition for generalized smooth optimization. Preliminary experiments show that our proposed algorithms perform favorably compared with existing methods

CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms

How to optimally dispatch orders to vehicles and how to tradeoff between immediate and future returns are fundamental questions for a typical ride-hailing platform. We model ride-hailing as a large-scale parallel ranking problem and study the joint decision-making task of order dispatching and fleet management in online ride-hailing platforms. This task brings unique challenges in the following four aspects. First, to facilitate a huge number of vehicles to act and learn efficiently and robustly, we treat each region cell as an agent and build a multi-agent reinforcement learning framework. Second, to coordinate the agents from different regions to achieve long-term benefits, we leverage the geographical hierarchy of the region grids to perform hierarchical reinforcement learning. Third, to deal with the heterogeneous and variant action space for joint order dispatching and fleet management, we design the action as the ranking weight vector to rank and select the specific order or the fleet management destination in a unified formulation. Fourth, to achieve the multi-scale ride-hailing platform, we conduct the decision-making process in a hierarchical way where a multi-head attention mechanism is utilized to incorporate the impacts of neighbor agents and capture the key agent in each scale. The whole novel framework is named as CoRide. Extensive experiments based on multiple cities real-world data as well as analytic synthetic data demonstrate that CoRide provides superior performance in terms of platform revenue and user experience in the task of city-wide hybrid order dispatching and fleet management over strong baselines.Comment: CIKM 201

Polyethylene glycol combined with lactulose has better efficacy than polyethylene glycol alone in bowel preparation before colonoscopy: A meta-analysis

Background: The accuracy of diagnosis and the safety of treatment in colonoscopy depends largely on the quality of bowel cleansing. This study aimed to compare the efficacy and adverse reactions of Polyethylene Glycol (PEG) combined with lactulose with that of PEG alone in bowel preparation before colonoscopy. Methods: The authors searched a number of databases including EMBASE, MEDLINE, Cochrane Library, and China Academic Journals Full-text Database. The authors screened according to literature inclusion and exclusion criteria, assessed the quality of the included literature, and extracted the data. The meta-analysis of included literature used RevMan 5.3 and Stata 14.0 software. Results: A total of 18 studies, including 2274 patients, were enrolled. The meta-analysis showed that PEG combined with lactulose had a better efficacy (OR = 3.87, 95% CI 3.07‒4.87, p = 0.000, and I2 = 36.2% in the efficiency group; WMD = 0.86, 95% CI 0.69‒1.03, p = 0.032 and I2 = 0% in the BBPS score group) in bowel preparation for patients with or without constipation. Moreover, PEG combined with lactulose had fewer adverse reactions, including abdominal pain (OR = 1.42, 95% CI 0.94‒2.14, p = 0.094), nausea (OR = 1.60, 95% CI 1.13‒2.28, p = 0.009) and vomiting (OR = 1.77, 95% CI 1.14‒2.74, p = 0.011), than PEG alone. No significant reduction in the incidence of abdominal distention was observed. Conclusion: PEG combined with lactulose may be a better choice for bowel preparation before colonoscopy compared with PEG alone

AutoPoster: A Highly Automatic and Content-aware Design System for Advertising Poster Generation

Advertising posters, a form of information presentation, combine visual and linguistic modalities. Creating a poster involves multiple steps and necessitates design experience and creativity. This paper introduces AutoPoster, a highly automatic and content-aware system for generating advertising posters. With only product images and titles as inputs, AutoPoster can automatically produce posters of varying sizes through four key stages: image cleaning and retargeting, layout generation, tagline generation, and style attribute prediction. To ensure visual harmony of posters, two content-aware models are incorporated for layout and tagline generation. Moreover, we propose a novel multi-task Style Attribute Predictor (SAP) to jointly predict visual style attributes. Meanwhile, to our knowledge, we propose the first poster generation dataset that includes visual attribute annotations for over 76k posters. Qualitative and quantitative outcomes from user studies and experiments substantiate the efficacy of our system and the aesthetic superiority of the generated posters compared to other poster generation methods.Comment: Accepted for ACM MM 202

Enhancing photoelectrochemical CO2 reduction with silicon photonic crystals

The effectiveness of silicon (Si) and silicon-based materials in catalyzing photoelectrochemistry (PEC) CO2 reduction is limited by poor visible light absorption. In this study, we prepared two-dimensional (2D) silicon-based photonic crystals (SiPCs) with circular dielectric pillars arranged in a square array to amplify the absorption of light within the wavelength of approximately 450 nm. By investigating five sets of n + p SiPCs with varying dielectric pillar sizes and periodicity while maintaining consistent filling ratios, our findings showed improved photocurrent densities and a notable shift in product selectivity towards CH4 (around 25% Faradaic Efficiency). Additionally, we integrated platinum nanoparticles, which further enhanced the photocurrent without impacting the enhanced light absorption effect of SiPCs. These results not only validate the crucial role of SiPCs in enhancing light absorption and improving PEC performance but also suggest a promising approach towards efficient and selective PEC CO2 reduction

Sex-Based Differences in Gut Microbiota Composition in Response to Tuna Oil and Algae Oil Supplementation in a D-galactose-Induced Aging Mouse Model

Our previous work indicated that a mixture of tuna oil and algae oil treatment in male mice effectively relieved D-galactose (D-gal)-induced aging and resulted in gut microbiota alterations, and that the best anti-aging effects were observed for a tuna oil to algae oil ratio of 1:2. However, the possibility of a sex-based difference in the anti-aging effect of the tuna oil and algae oil mixture or gut microbiota variation, has rarely been investigated. In this study, the anti-aging effect of an oil mixture (1:2) in male and female mice was measured, and oil treatment improved the learning and cognition of mice that were damaged by D-gal, increased the activities of anti-oxidative enzymes, and decreased the level of MDA, which acted as a hallmark of oxidative damage to lipids. Male mice showed better anti-aging effects than female mice with a specific oil mixture ratio, and the clinical drug donepezil showed a similar or better effect on aging alleviation than oil treatments in both sexes. On the other hand, the same oil treatment led to different gut microbiota composition alterations in male and female mice. Redundancy analysis (RDA) identified 31 and 30 key operational taxonomic units (OTUs) in the male and female mice, respectively, and only three of these OTUs overlapped. Moreover, the abundance of Lactobacillus and several probiotic-like butyric acid producers was higher in male mice than in female mice, whereas the abundance of some inflammation-related genera, such as Clostridium XlVa, was lower in male mice. In conclusion, this study indicated the sex-based differences related to the anti-aging effects of tuna oil and algae oil treatment are accompanied by sex-based differences in gut microbiota modulation

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Effect of Ce-modified Fe/ZSM-5 zeolite for selective catalytic reduction of NO

A series of xCe-Fe/ZSM-5 (x = 0, 0.25, 0.5 wt%) samples were prepared by the impregnation method, and the catalytic activity was evaluated by the selective catalytic reduction of NOx with ammonia (NH3-SCR). The physicochemical properties of prepared samples were characterized by various techniques such as X-ray diffraction (XRD), Brunner-Emmet-Teller (BET) measurement, hydrogen temperatureprogrammed reduction (H2-TPR), X-ray photoelectron spectroscopy (XPS), ammonia temperatureprogrammed desorption (NH3-TPD) and in situ diffuse reflectance infrared Fourier transform spectroscopy (in situ DRIFTS). XRD and BET results demonstrated that Ce and Fe species were uniform dispersed on the surface of the ZSM-5 zeolite and the micropore structure of ZSM-5 was still maintained. H2-TPR analysis indicated that the doping of Ce created more isolated Ce4+ and Fe3+ on the surface of catalysts, and the abundant Ce4+ and Fe3+ could enhance the reduction ability of catalysts. XPS analysis suggested that the doping of Ce could generate more oxygen vacancies, thereby increasing the number of chemisorption oxygen. According to the in-situ DRIFTS and NH3-TPD results, Ce species provided more acidic sites, which is beneficial to the NH3 adsorption ability of ZSM-5 zeolite. Additionally, the abundant chemisorption oxygen, medium and strong Brønsted acid sites, excellent NH3 adsorption ability and outstanding reduction property are beneficial to the NH3-SCR reaction. Among all prepared samples, the 0.25Ce-Fe/ZSM-5 sample possessed the widest reaction temperature window and the best catalytic performance (NO conversion over 98% at 350-450 °C), which was associated with the abundant acid sites and remarkable adsorption ability of NH3, outstanding redox ability and abundant chemisorption oxygen after the doping of Ce

Data-Driven Flower Petal Modeling with Botany Priors

In this paper we focus on the 3D modeling of flower, in particular the petals. The complex structure, severe occlu-sions, and wide variations make the reconstruction of their 3D models a challenging task. Therefore, even though the flower is the most distinctive part of a plant, there has been little modeling study devoted to it. We overcome these chal-lenges by combining data driven modeling techniques with domain knowledge from botany. Taking a 3D point cloud of an input flower scanned from a single view, our method starts with a level-set based segmentation of each individ-ual petal, using both appearance and 3D information. Each segmented petal is then fitted with a scale-invariant mor-phable petal shape model, which is constructed from indi-vidually scanned exemplar petals. Novel constraints based on botany studies, such as the number and spatial layout of petals, are incorporated into the fitting process for realisti-cally reconstructing occluded regions and maintaining cor-rect 3D spatial relations. Finally, the reconstructed petal shape is texture mapped using the registered color images, with occluded regions filled in by content from visible ones. Experiments show that our approach can obtain realistic modeling of flowers even with severe occlusions and large shape/size variations. 1