A New synthesis of triazolo[4,5-g]quinolines and unexpected ring reduced products by treatment with hydrazine hydrate

A new synthesis of the linear heterocycle 4-chloro-1H-triazolo[4,5-g]quinoline by reduction of the novel compound 4-chloro-1H-triazolo[4,5-g]quinoline-1-oxide is reported. Treatment of the latter with hydrazine hydrate in ethanol in a sealed steel vessel in the presence or not of palladised charcoal, under various conditions of both time and temperature, afforded some derivatives of both ring reduction and ring construction

Linear Memory Networks

Recurrent neural networks can learn complex transduction problems that require maintaining and actively exploiting a memory of their inputs. Such models traditionally consider memory and input-output functionalities indissolubly entangled. We introduce a novel recurrent architecture based on the conceptual separation between the functional input-output transformation and the memory mechanism, showing how they can be implemented through different neural components. By building on such conceptualization, we introduce the Linear Memory Network, a recurrent model comprising a feedforward neural network, realizing the non-linear functional transformation, and a linear autoencoder for sequences, implementing the memory component. The resulting architecture can be efficiently trained by building on closed-form solutions to linear optimization problems. Further, by exploiting equivalence results between feedforward and recurrent neural networks we devise a pretraining schema for the proposed architecture. Experiments on polyphonic music datasets show competitive results against gated recurrent networks and other state of the art models

Sintesi di nuovi sistemi triciclici aromatici lineari. Triazolochinoloni, imidazochinoloni e 4-osso-piridochinossaline quali potenziali agenti DNA intercalanti

Recentemente abbiamo riportato la sintesi di alcuni nuovi sistemi triciclici lineari aromatici azotati che per la loro tipicità, una volta opportunamente funzionalizzati, potranno essere saggiati al fine di valutarne la potenzialità farmacologia

Il «Marciume molle» dei frutti di pomodoro da <i>Pseudomonas virdiflava</i> (Burkholder) Dowson

The Authors report on the results of researches about a serious soft rot of greenhouse tomato fruits in southern Sardinia (Italy). The disease symptoms and the characteristics of the causal agent, identified by its morphological and cultural characters, and by biochemical and serological lests, as Pseudomonas viridiflava (Burkholder) Dowson, are described

Prove di lotta contro il <i>Cladosporium cucumerinum</i> Ell. <i>et</i> Arth. agente della «cladosporiosi» dello zucchino

The results of control trials against Cladosporium cucumerinum Ell. et Arth. artificially inoculated on aged pumpkin plants and in soil belore seeding, are referred. All the tested fungicides (benomyl, captafol, chlorothalonil, iprodione, mancozeb, procymidone, thiram, thyophanate-methyl), although with dlfference according to the kind of treatment, limited infections powerfully. Thyophanate-methyl and benomyl in foliar sprays, thyophanate-methyl and chlorothalonil in soil drench, thyophanate-methyl, benomyl and captafol in seed dressing, gave best results. By these results, tha Authors point out that a very good control of pumpkin scab is attainable avoiding primary infections on seedlings by soil drench and se ed dressing

La «Cladosporiosi» dello zucchino (<i>Cucurbita pepo</i> L.) in coltura protetta in Sardegna

On last years the «scab» of pumpkin (Cucurbita pepo L.) induced by Cladosporium cucumerinum Ell. et Arth. has been observed very damaging on greenhouse cultures in southern Sardinia (Italy). The disease symptoms and epidemiology, as well as the pathogen's characters, are described. Finally, control means are briefly reviewed

L'«Alternariosi» del cartamo (<i>Carthamus tinctorius</i> L.) da <i>Alternaria cartami</i> Ch. in Sardegna

A stem and leaf spot of safflower by Alternaria carthami Ch. has been found on last three years in Sardinia (Italy). The disease symptoms and the pathogen characters are described. The susceptibility of 7 safflower expe rimental cvs. has been assessed, and by artificial inoculations the fungus pathogenicity was checked. Finally, the Authors examine some epidemiological aspects of the disese and briefly expose the control means

Incremental Training of a Recurrent Neural Network Exploiting a Multi-Scale Dynamic Memory

The effectiveness of recurrent neural networks can be largely influenced by their ability to store into their dynamical memory information extracted from input sequences at different frequencies and timescales. Such a feature can be introduced into a neural architecture by an appropriate modularization of the dynamic memory. In this paper we propose a novel incrementally trained recurrent architecture targeting explicitly multi-scale learning. First, we show how to extend the architecture of a simple RNN by separating its hidden state into different modules, each subsampling the network hidden activations at different frequencies. Then, we discuss a training algorithm where new modules are iteratively added to the model to learn progressively longer dependencies. Each new module works at a slower frequency than the previous ones and it is initialized to encode the subsampled sequence of hidden activations. Experimental results on synthetic and real-world datasets on speech recognition and handwritten characters show that the modular architecture and the incremental training algorithm improve the ability of recurrent neural networks to capture long-term dependencies.Comment: accepted @ ECML 2020. arXiv admin note: substantial text overlap with arXiv:2001.1177

Continual Learning with Gated Incremental Memories for sequential data processing

The ability to learn in dynamic, nonstationary environments without forgetting previous knowledge, also known as Continual Learning (CL), is a key enabler for scalable and trustworthy deployments of adaptive solutions. While the importance of continual learning is largely acknowledged in machine vision and reinforcement learning problems, this is mostly under-documented for sequence processing tasks. This work proposes a Recurrent Neural Network (RNN) model for CL that is able to deal with concept drift in input distribution without forgetting previously acquired knowledge. We also implement and test a popular CL approach, Elastic Weight Consolidation (EWC), on top of two different types of RNNs. Finally, we compare the performances of our enhanced architecture against EWC and RNNs on a set of standard CL benchmarks, adapted to the sequential data processing scenario. Results show the superior performance of our architecture and highlight the need for special solutions designed to address CL in RNNs.Comment: Accepted as a conference paper at 2020 International Joint Conference on Neural Networks (IJCNN 2020). Part of 2020 IEEE World Congress on Computational Intelligence (IEEE WCCI 2020