Density Functional for Anisotropic Fluids

We propose a density functional for anisotropic fluids of hard body particles. It interpolates between the well-established geometrically based Rosenfeld functional for hard spheres and the Onsager functional for elongated rods. We test the new approach by calculating the location of the the nematic-isotropic transition in systems of hard spherocylinders and hard ellipsoids. The results are compared with existing simulation data. Our functional predicts the location of the transition much more accurately than the Onsager functional, and almost as good as the theory by Parsons and Lee. We argue that it might be suited to study inhomogeneous systems.Comment: To appear in J. Physics: Condensed Matte

Batch Clipping and Adaptive Layerwise Clipping for Differential Private Stochastic Gradient Descent

Each round in Differential Private Stochastic Gradient Descent (DPSGD) transmits a sum of clipped gradients obfuscated with Gaussian noise to a central server which uses this to update a global model which often represents a deep neural network. Since the clipped gradients are computed separately, which we call Individual Clipping (IC), deep neural networks like resnet-18 cannot use Batch Normalization Layers (BNL) which is a crucial component in deep neural networks for achieving a high accuracy. To utilize BNL, we introduce Batch Clipping (BC) where, instead of clipping single gradients as in the orginal DPSGD, we average and clip batches of gradients. Moreover, the model entries of different layers have different sensitivities to the added Gaussian noise. Therefore, Adaptive Layerwise Clipping methods (ALC), where each layer has its own adaptively finetuned clipping constant, have been introduced and studied, but so far without rigorous DP proofs. In this paper, we propose {\em a new ALC and provide rigorous DP proofs for both BC and ALC}. Experiments show that our modified DPSGD with BC and ALC for CIFAR-$10$ with resnet-$18$ converges while DPSGD with IC and ALC does not.Comment: 20 pages, 18 Figure

Generalizing DP-SGD with Shuffling and Batch Clipping

Classical differential private DP-SGD implements individual clipping with random subsampling, which forces a mini-batch SGD approach. We provide a general differential private algorithmic framework that goes beyond DP-SGD and allows any possible first order optimizers (e.g., classical SGD and momentum based SGD approaches) in combination with batch clipping, which clips an aggregate of computed gradients rather than summing clipped gradients (as is done in individual clipping). The framework also admits sampling techniques beyond random subsampling such as shuffling. Our DP analysis follows the $f$-DP approach and introduces a new proof technique which allows us to derive simple closed form expressions and to also analyse group privacy. In particular, for $E$ epochs work and groups of size $g$, we show a $\sqrt{g E}$ DP dependency for batch clipping with shuffling.Comment: Update disclaimer

Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes

Hogwild! implements asynchronous Stochastic Gradient Descent (SGD) where multiple threads in parallel access a common repository containing training data, perform SGD iterations and update shared state that represents a jointly learned (global) model. We consider big data analysis where training data is distributed among local data sets in a heterogeneous way -- and we wish to move SGD computations to local compute nodes where local data resides. The results of these local SGD computations are aggregated by a central "aggregator" which mimics Hogwild!. We show how local compute nodes can start choosing small mini-batch sizes which increase to larger ones in order to reduce communication cost (round interaction with the aggregator). We improve state-of-the-art literature and show $O(\sqrt{K}$) communication rounds for heterogeneous data for strongly convex problems, where $K$ is the total number of gradient computations across all local compute nodes. For our scheme, we prove a \textit{tight} and novel non-trivial convergence analysis for strongly convex problems for {\em heterogeneous} data which does not use the bounded gradient assumption as seen in many existing publications. The tightness is a consequence of our proofs for lower and upper bounds of the convergence rate, which show a constant factor difference. We show experimental results for plain convex and non-convex problems for biased (i.e., heterogeneous) and unbiased local data sets.Comment: arXiv admin note: substantial text overlap with arXiv:2007.09208 AISTATS 202

Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: an inter-laboratory study.

Antimicrobial resistance (AMR) poses a threat to public health. Clinical microbiology laboratories typically rely on culturing bacteria for antimicrobial-susceptibility testing (AST). As the implementation costs and technical barriers fall, whole-genome sequencing (WGS) has emerged as a 'one-stop' test for epidemiological and predictive AST results. Few published comparisons exist for the myriad analytical pipelines used for predicting AMR. To address this, we performed an inter-laboratory study providing sets of participating researchers with identical short-read WGS data from clinical isolates, allowing us to assess the reproducibility of the bioinformatic prediction of AMR between participants, and identify problem cases and factors that lead to discordant results. We produced ten WGS datasets of varying quality from cultured carbapenem-resistant organisms obtained from clinical samples sequenced on either an Illumina NextSeq or HiSeq instrument. Nine participating teams ('participants') were provided these sequence data without any other contextual information. Each participant used their choice of pipeline to determine the species, the presence of resistance-associated genes, and to predict susceptibility or resistance to amikacin, gentamicin, ciprofloxacin and cefotaxime. We found participants predicted different numbers of AMR-associated genes and different gene variants from the same clinical samples. The quality of the sequence data, choice of bioinformatic pipeline and interpretation of the results all contributed to discordance between participants. Although much of the inaccurate gene variant annotation did not affect genotypic resistance predictions, we observed low specificity when compared to phenotypic AST results, but this improved in samples with higher read depths. Had the results been used to predict AST and guide treatment, a different antibiotic would have been recommended for each isolate by at least one participant. These challenges, at the final analytical stage of using WGS to predict AMR, suggest the need for refinements when using this technology in clinical settings. Comprehensive public resistance sequence databases, full recommendations on sequence data quality and standardization in the comparisons between genotype and resistance phenotypes will all play a fundamental role in the successful implementation of AST prediction using WGS in clinical microbiology laboratories

Impacts of fallow conditions, compost and silicate fertilizer on soil nematode community in salt–affected paddy rice fields in acid sulfate and alluvial soils in the Mekong Delta, Vietnam

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)Avoidance of intensive rice cultivation (IRC) and soil amendments are potential practices to enhance soil properties. There is only limited information on the effects of reduced IRC and its mixture with compost or silicate fertilizer (Si) on the soil nematode community in salt–affected soils. This study aimed to assess the shifts of soil nematode community by reducing a rice crop from triple rice system (RRR) to a double rice system and mixed with compost or Si in paddy fields in acid sulfate soil (ASS) and alluvial soil (AL) in the Mekong Delta, Vietnam. Field experiments were designed with four treatments in four replicates, including RRR and a proposed system of double–rice followed by a fallow (FRR) and with 3 Mg ha–1 crop−1 compost or 100 kg ha–1 crop−1 Si. Soils were collected at harvest after the 2 year experiment, reflecting the fifth and third consecutive rice crop in RRR and FRR system, respectively. Results showed that reduced IRC gave a significant reduction in abundance of plant–parasitic nematodes (PPN), dominated by Hirschmanniella and increased abundance bacterivorous nematodes when mixed to compost and silicate fertilizer in ASS. In addition, reduced IRC increased nematode biodiversity Hill’s indices and reduced herbivorous footprint in ASS. Proposed system having compost or Si had strongly increased in bacterivorous and omnivorous footprints. Particularly, reduced IRC mixture with Si increased abundance of Rhabdolaimus, Mesodorylaimus and Aquatides, metabolic footprints (structure footprint, bacterivorous, omnivorous and predator) and diversity Hill’s N1 index in ASS. Our results highlighted that reduced IRC was a beneficial practice for decreasing abundance of PPN in salt-affected soils and increasing abundance of FLN in ASS. IRC mixture with compost or Si had potential in structuring the nematode communities with increasing biodiversity, trophic structure, and metabolic footprintsPeer reviewedFinal Accepted Versio

QUANTITATIVE DETERMINATION AND PREPARATIVE ISOLATION OF TWO MAJOR ALKALOIDS FROM THE VIETNAMESE MEDICINAL HERB EVODIAE FRUCTUS

Objective: To develop a simple and accurate HPLC-DAD method for simultaneous determination, the content of major components: limonin, evodiamine, and rutaecarpine in Evodiae fructus and evaluation the quality of Evodiae fructus sold in markets. Methods: Open column chromatography was used to separate and purify rutaecarpine and evodiamine, the two major alkaloids from Evodiae fructus extract as a laboratory standard. Chromatographic separation was achieved using a Germini C18 column (150 mm × 4.6 mm I.D., 5 µm), detected at 210 nm. The mobile phase consisted of acetonitrile (A), methanol (B), and water (C). The validated method simultaneously determined alkaloid content in 40 batches of samples collected from markets in different regions of Vietnam. Results: In one-step purification, our method yielded 326 mg of rutaecarpine and 128 mg of evodiamine from 3.2 g of crude extract, with purities of 98.9 and 98.5%, respectively. The structures of these compounds were identified using 1H NMR and 13C NMR. There was a significant correlation between alkaloid content and fruit size, with a Spearman correlation coefficient of>0.5 (p<0.001), and there was a large difference in alkaloid contents between three maturity degrees of the fruit. Open-mouth fruits and fruits with average sizes of 4 to 6 mm had the highest alkaloid contents, whereas closed-mouth fruits had the lowest. Conclusion: This study provided information on the standardization and quality control of evodiamine and rutaecarpine in Evodiae fructus, as well as a foundation for further pharmacological and toxicological studies

Influence maximization under fairness budget distribution in online social networks

In social influence analysis, viral marketing, and other fields, the influence maximization problem is a fundamental one with critical applications and has attracted many researchers in the last decades. This problem asks to find a k-size seed set with the largest expected influence spread size. Our paper studies the problem of fairness budget distribution in influence maximization, aiming to find a seed set of size k fairly disseminated in target communities. Each community has certain lower and upper bounded budgets, and the number of each community's elements is selected into a seed set holding these bounds. Nevertheless, resolving this problem encounters two main challenges: strongly influential seed sets might not adhere to the fairness constraint, and it is an NP-hard problem. To address these shortcomings, we propose three algorithms (FBIM1, FBIM2, and FBIM3). These algorithms combine an improved greedy strategy for selecting seeds to ensure maximum coverage with the fairness constraints by generating sampling through a Reverse Influence Sampling framework. Our algorithms provide a (1/2 - epsilon)-approximation of the optimal solution, and require O(kT log ((8 + 2 epsilon)n ln + 2/delta + ln(nk)/epsilon(2))), O(kT log n/epsilon(2)k), and O(T/epsilon log k/epsilon log n/epsilon(2)k) complexity, respectively. We conducted experiments on real social networks. The result shows that our proposed algorithms are highly scalable while satisfying theoretical assurances, and that the coverage ratios with respect to the target communities are larger than those of the state-of-the-art alternatives; there are even cases in which our algorithms reaches 100% coverage with respect to target communities. In addition, our algorithms are feasible and effective even in cases involving big data; in particular, the results of the algorithms guarantee fairness constraints.Web of Science1022art. no. 418