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  1. Federated Averaging (FedAvg) and its variants are the most popular optimization algorithms in federated learning (FL). Previous convergence analyses of FedAvg either assume full client participation or partial client participation where the clients can be uniformly sampled. However, in practical cross-device FL systems, only a subset of clients that satisfy local criteria such as battery status, network connectivity, and maximum participation frequency requirements (to ensure privacy) are available for training at a given time. As a result, client availability follows a natural cyclic pattern. We provide (to our knowledge) the first theoretical framework to analyze the convergence of FedAvg with cyclic client participation with several different client optimizers such as GD, SGD, and shuffled SGD. Our analysis discovers that cyclic client participation can achieve a faster asymptotic convergence rate than vanilla FedAvg with uniform client participation under suitable conditions, providing valuable insights into the design of client sampling protocols. 
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    Free, publicly-accessible full text available July 23, 2024
  2. Alloying in two-dimensional (2D) transition metal dichalcogenides (TMD) has allowed bandgap engineering and phase transformation, which provide more flexibility and functionality for electronic and photonic devices. To date, many ternary TMD alloys with homogenous compositions have been synthesized. However, realization of bandgap modulation spatially within a single TMD nanosheet remains largely unexplored. In this work, we demonstrate the synthesis of spatially composition-graded WSe2xTe2-2x flakes using an in situ chemical vapor deposition method. The photoluminescence and Raman spectra line-scanning characterization indicate a spatially graded bandgap, which increases from 1.46 eV (center) to 1.61 eV (edge) within one monolayer flake. Furthermore, the electronic devices based on this spatially graded material exhibit tunable transfer characteristics. 
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  3. The rapid growth of GPS technology and mobile devices has led to a massive accumulation of location data, bringing considerable benefits to individuals and society. One of the major usages of such data is travel time prediction, a typical service provided by GPS navigation devices and apps. Meanwhile, the constant collection and analysis of the individual location data also pose unprecedented privacy threats. We leverage the notion of geo-indistinguishability, an extension of differential privacy to the location privacy setting, and propose a procedure for privacy-preserving travel time prediction without collecting actual individual GPS trace data. We propose new concepts to examine the impact of the geo-indistinguishability sanitization on the usefulness of GPS traces and provide analytical and experimental utility analysis for privacy-preserving travel time prediction. We also propose new metrics to measure the adversary error in learning individual GPS traces from the collected sanitized data. Our experiment results suggest that the proposed procedure provides travel time analysis with satisfactory accuracy at reasonably small privacy costs. 
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  4. This paper studies how neural network architecture affects the speed of training. We introduce a simple concept called gradient confusion to help formally analyze this. When gradient confusion is high, stochastic gradients produced by different data samples may be negatively correlated, slowing down convergence. But when gradient confusion is low, data samples interact harmoniously, and training proceeds quickly. Through theoretical and experimental results, we demonstrate how the neural network architecture affects gradient confusion, and thus the efficiency of training. Our results show that, for popular initialization techniques, increasing the width of neural networks leads to lower gradient confusion, and thus faster model training. On the other hand, increasing the depth of neural networks has the opposite effect. Our results indicate that alternate initialization techniques or networks using both batch normalization and skip connections help reduce the training burden of very deep networks. 
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  6. This study describes the evaluation of a range of approaches to semantic segmentation of hyperspectral images of sorghum plants, classifying each pixel as either nonplant or belonging to one of the three organ types (leaf, stalk, panicle). While many current methods for segmentation focus on separating plant pixels from background, organ-specific segmentation makes it feasible to measure a wider range of plant properties. Manually scored training data for a set of hyperspectral images collected from a sorghum association population was used to train and evaluate a set of supervised classification models. Many algorithms show acceptable accuracy for this classification task. Algorithms trained on sorghum data are able to accurately classify maize leaves and stalks, but fail to accurately classify maize reproductive organs which are not directly equivalent to sorghum panicles. Trait measurements extracted from semantic segmentation of sorghum organs can be used to identify both genes known to be controlling variation in a previously measured phenotypes (e.g., panicle size and plant height) as well as identify signals for genes controlling traits not previously quantified in this population (e.g., stalk/leaf ratio). Organ level semantic segmentation provides opportunities to identify genes controlling variation in a wide range of morphological phenotypes in sorghum, maize, and other related grain crops. 
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  7. Abstract. Oxygenated organic molecules (OOMs) are the crucial intermediates linkingvolatile organic compounds (VOCs) to secondary organic aerosols (SOAs) in theatmosphere, but comprehensive understanding of the characteristics of OOMsand their formation from VOCs is still missing. Ambient observations ofOOMs using recently developed mass spectrometry techniques are stilllimited, especially in polluted urban atmospheres where VOCs and oxidants areextremely variable and complex. Here, we investigate OOMs, measured by anitrate-ion-based chemical ionization mass spectrometer at Nanjing ineastern China, through performing positive matrix factorization on binnedmass spectra (binPMF). The binPMF analysis reveals three factors aboutanthropogenic VOC (AVOC) daytime chemistry, three isoprene-relatedfactors, three factors about biogenic VOC (BVOC) nighttime chemistry, andthree factors about nitrated phenols. All factors are influenced by NOxin different ways and to different extents. Over 1000 non-nitro moleculeshave been identified and then reconstructed from the selected solution ofbinPMF, and about 72 % of the total signals are contributed bynitrogen-containing OOMs, mostly regarded as organic nitrates formed throughperoxy radicals terminated by nitric oxide or nitrate-radical-initiatedoxidations. Moreover, multi-nitrates account for about 24 % of the totalsignals, indicating the significant presence of multiple generations,especially for isoprene (e.g., C5H10O8N2 andC5H9O10N3). Additionally, the distribution of OOMconcentration on the carbon number confirms their precursors are driven by AVOCsmixed with enhanced BVOCs during summer. Our results highlight the decisiverole of NOx in OOM formation in densely populated areas, and we encouragemore studies on the dramatic interactions between anthropogenic and biogenicemissions. 
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