Over-the-Air Computation Aided Federated Learning with the Aggregation of Normalized Gradient

Over-the-air computation is a communication-efficient solution for federated learning (FL). In such a system, iterative procedure is performed: Local gradient of private loss function is updated, amplified and then transmitted by every mobile device; the server receives the aggregated gradient all-at-once, generates and then broadcasts updated model parameters to every mobile device. In terms of amplification factor selection, most related works suppose the local gradient's maximal norm always happens although it actually fluctuates over iterations, which may degrade convergence performance. To circumvent this problem, we propose to turn local gradient to be normalized one before amplifying it. Under our proposed method, when the loss function is smooth, we prove our proposed method can converge to stationary point at sub-linear rate. In case of smooth and strongly convex loss function, we prove our proposed method can achieve minimal training loss at linear rate with any small positive tolerance. Moreover, a tradeoff between convergence rate and the tolerance is discovered. To speedup convergence, problems optimizing system parameters are also formulated for above two cases. Although being non-convex, optimal solution with polynomial complexity of the formulated problems are derived. Experimental results show our proposed method can outperform benchmark methods on convergence performance

Homologous self-organising scale-invariant properties characterise long range species spread and cancer invasion

The invariance of some system properties over a range of temporal and/or spatial scales is an attribute of many processes in nature1, often characterised by power law functions and fractal geometry2. In particular, there is growing consensus in that fat-tailed functions like the power law adequately describe long-distance dispersal (LDD) spread of organisms 3,4. Here we show that the spatial spread of individuals governed by a power law dispersal function is represented by a clear and unique signature, characterised by two properties: A fractal geometry of the boundaries of patches generated by dispersal with a fractal dimension D displaying universal features, and a disrupted patch size distribution characterised by two different power laws. Analysing patterns obtained by simulations and real patterns from species dispersal and cell spread in cancer invasion we show that both pattern properties are a direct result of LDD and localised dispersal and recruitment, reflecting population self-organisation

Federated Learning Robust to Byzantine Attacks: Achieving Zero Optimality Gap

In this paper, we propose a robust aggregation method for federated learning (FL) that can effectively tackle malicious Byzantine attacks. At each user, model parameter is firstly updated by multiple steps, which is adjustable over iterations, and then pushed to the aggregation center directly. This decreases the number of interactions between the aggregation center and users, allows each user to set training parameter in a flexible way, and reduces computation burden compared with existing works that need to combine multiple historical model parameters. At the aggregation center, geometric median is leveraged to combine the received model parameters from each user. Rigorous proof shows that zero optimality gap is achieved by our proposed method with linear convergence, as long as the fraction of Byzantine attackers is below half. Numerical results verify the effectiveness of our proposed method

How does environmental regulation affect firm innovation? Evidence based on corporate life cycle

Environmental regulation can be an effective tool for the government to balance the relationship between the ecology and economic development. In this paper, the corporate life cycle theory is incorporated into environmental regulation policy evaluation, and the impact of environmental regulation on the output of innovation by China’s enterprises in different development stages is analyzed. The results show that environmental regulation significantly promotes innovation and particularly green innovation of all enterprises in China. The effect is especially strong for enterprises in the start-up and growing stage, and the impetus for innovation among private enterprises is significantly greater than that of state-owned enterprises. The mechanism behind these results is also analyzed. This paper extends the existing theoretical framework and provides empirical reference for future environmental policy research. Also, we provide guidance for the government to formulate environmental policies according to the development tasks of different stages and the nature of different enterprises, so as to achieve the social and economic goals effectively

Local Energy based Image Fusion in Sharp Frequency Localized Contourlet Transform

Image fusion method based on multiscale transform (MST) is a popular choice in recent research. Sharp frequency localized contourlet transform (SFLCT) that significantly outperform the original contourlet transform is proposed. Commonly, the upsamplers and the downsamplers presented in directional filter banks of SFLCT make the resulting image not shift-invariant and easily cause the pseudo-Gibbs phenomena. In order to suppress the pseudo-Gibbs phenomena, we apply cycle spinning as compensation. Then, the coefficients of shifted images are calculated. We take the following image fusion rules. First, cycle spinning the source images, the shifted images are obtained. Second, selecting the low-frequency coefficients by the local energy method and calculating the high-frequency coefficients by the sum modified Laplacian (SML), and the coefficients fusion follows. Third, applying the inverse SFLCT and the inverse cycle-spinning sequentially, the image is reconstructed. Numerical experiment results show that the proposed method significantly outperform the wavelet transform, the pyramid transform and the curvelet transform both in visual quality and in quantitative analysis

Integrated analysis of transcriptomics and metabolomics of peach under cold stress

Low temperature is one of the environmental factors that restrict the growth and geographical distribution of peach (Prunus persica L. Batsch). To explore the molecular mechanisms of peach brunches in response to cold, we analyzed the metabolomics and transcriptomics of ‘Donghe No.1’ (cold-tolerant, CT) and ‘21st Century’ (cold-sensitive, CS) treated by different temperatures (-5 to -30°C) for 12 h. Some cold-responsive metabolites (e.g., saccharides, phenolic acids and flavones) were identified with upregulation only in CT. Further, we identified 1991 cold tolerance associated genes in these samples and they were significantly enriched in the pathways of ‘galactose metabolism’, ‘phenylpropanoid biosynthesis’ and ‘flavonoids biosynthesis’. Weighted gene correlation network analysis showed that soluble sugar, flavone, and lignin biosynthetic associated genes might play a key role in the cold tolerance of peach. In addition, several key genes (e.g., COMT, CCR, CAD, PER and F3’H) were substantially expressed more in CT than CS under cold stress, indicating that they might be major factors during the adaptation of peach to low temperature. This study will not only improve our understanding towards the molecular mechanisms of peach trees under cold stress but also contribute to the screening and breeding program of peach in the future

Battle of Postdisaster Response and Restoration

[EN] The paper presents the results of the Battle of Postdisaster Response and Restoration (BPDRR) presented in a special session at the first International water distribution systems analysis & computing and control in the water industry (WDSA/CCWI) Joint Conference, held in Kingston, Ontario, Canada, in July 2018. The BPDRR problem focused on how to respond and restore water service after the occurrence of five earthquake scenarios that cause structural damage in a water distribution system. Participants were required to propose a prioritization schedule to fix the damages of each scenario while following restrictions on visibility/nonvisibility of damages. Each team/approach was evaluated against six performance criteria: (1) time without supply for hospital/firefighting, (2) rapidity of recovery, (3) resilience loss, (4) average time of no user service, (5) number of users without service for eight consecutive hours, and (6) water loss. Three main types of approaches were identified from the submissions: (1) general-purpose metaheuristic algorithms, (2) greedy algorithms, and (3) ranking-based prioritizations. All three approaches showed potential to solve the challenge efficiently. The results of the participants showed that for this network, the impact of a large-diameter pipe failure on the network is more significant than several smaller pipes failures. The location of isolation valves and the size of hydraulic segments influenced the resilience of the system during emergencies. On average, the interruptions to water supply (hospitals and firefighting) varied considerably among solutions and emergency scenarios, highlighting the importance of private water storage for emergencies. 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Kingston Canada: Open Journal Systems

Comparative genomic and transcriptomic analysis revealed genetic characteristics related to solvent formation and xylose utilization in Clostridium acetobutylicum EA 2018

<p>Abstract</p> <p>Background</p> <p><it>Clostridium acetobutylicum</it>, a gram-positive and spore-forming anaerobe, is a major strain for the fermentative production of acetone, butanol and ethanol. But a previously isolated hyper-butanol producing strain <it>C. acetobutylicum </it>EA 2018 does not produce spores and has greater capability of solvent production, especially for butanol, than the type strain <it>C. acetobutylicum </it>ATCC 824.</p> <p>Results</p> <p>Complete genome of <it>C. acetobutylicum </it>EA 2018 was sequenced using Roche 454 pyrosequencing. Genomic comparison with ATCC 824 identified many variations which may contribute to the hyper-butanol producing characteristics in the EA 2018 strain, including a total of 46 deletion sites and 26 insertion sites. In addition, transcriptomic profiling of gene expression in EA 2018 relative to that of ATCC824 revealed expression-level changes of several key genes related to solvent formation. For example, <it>spo0A </it>and <it>adhEII </it>have higher expression level, and most of the acid formation related genes have lower expression level in EA 2018. Interestingly, the results also showed that the variation in CEA_G2622 (CAC2613 in ATCC 824), a putative transcriptional regulator involved in xylose utilization, might accelerate utilization of substrate xylose.</p> <p>Conclusions</p> <p>Comparative analysis of <it>C. acetobutylicum </it>hyper-butanol producing strain EA 2018 and type strain ATCC 824 at both genomic and transcriptomic levels, for the first time, provides molecular-level understanding of non-sporulation, higher solvent production and enhanced xylose utilization in the mutant EA 2018. The information could be valuable for further genetic modification of <it>C. acetobutylicum </it>for more effective butanol production.</p