Deep learning systems as complex networks

Thanks to the availability of large scale digital datasets and massive amounts of computational power, deep learning algorithms can learn representations of data by exploiting multiple levels of abstraction. These machine learning methods have greatly improved the state-of-the-art in many challenging cognitive tasks, such as visual object recognition, speech processing, natural language understanding and automatic translation. In particular, one class of deep learning models, known as deep belief networks, can discover intricate statistical structure in large data sets in a completely unsupervised fashion, by learning a generative model of the data using Hebbian-like learning mechanisms. Although these self-organizing systems can be conveniently formalized within the framework of statistical mechanics, their internal functioning remains opaque, because their emergent dynamics cannot be solved analytically. In this article we propose to study deep belief networks using techniques commonly employed in the study of complex networks, in order to gain some insights into the structural and functional properties of the computational graph resulting from the learning process.Comment: 20 pages, 9 figure

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

Optically induced spin to charge transduction in donor spin read-out

The proposed read-out configuration D+D- for the Kane Si:P architecture[Nature 393, 133 (1998)] depends on spin-dependent electron tunneling between donors, induced adiabatically by surface gates. However, previous work has shown that since the doubly occupied donor state is so shallow the dwell-time of the read-out state is less than the required time for measurement using a single electron transistor (SET). We propose and analyse single-spin read-out using optically induced spin to charge transduction, and show that the top gate biases, required for qubit selection, are significantly less than those demanded by the Kane scheme, thereby increasing the D+D- lifetime. Implications for singlet-triplet discrimination for electron spin qubits are also discussed.Comment: 8 pages, 10 figures; added reference, corrected typ

A precise CNOT gate in the presence of large fabrication induced variations of the exchange interaction strength

We demonstrate how using two-qubit composite rotations a high fidelity controlled-NOT (CNOT) gate can be constructed, even when the strength of the interaction between qubits is not accurately known. We focus on the exchange interaction oscillation in silicon based solid-state architectures with a Heisenberg Hamiltonian. This method easily applies to a general two-qubit Hamiltonian. We show how the robust CNOT gate can achieve a very high fidelity when a single application of the composite rotations is combined with a modest level of Hamiltonian characterisation. Operating the robust CNOT gate in a suitably characterised system means concatenation of the composite pulse is unnecessary, hence reducing operation time, and ensuring the gate operates below the threshold required for fault-tolerant quantum computation.Comment: 9 pages, 8 figure

Gametic and somatic embryogenesis through in vitro anther culture of different Citrus genotypes

Abstract: In vitro tissue culture represents a useful technique for advancing Citrus breeding and propagation. Among in vitro regeneration systems, anther culture is commonly used to produce haploids and doubled haploids for a fast-track producing homozygous lines, in comparison with the traditional self-pollination approach, which involves several generations of selfing. In addition, anthers culture can produce somatic embryos that can also be used for clonal propagation. In this study, two thermal shocks were applied to the anthers of six Citrus genotypes (two clementine and four sweet oranges), just after they were put in culture. The response obtained was different depending on the genotype: both clementines, namely Hernandina and Corsica, produced homozygous and triploid regenerants (microspore-derived embryos), whereas all of the analyzed regenerants from sweet oranges, three cultivars of Tarocco and Moro, produced heterozygous and diploid regenerants similar to the parental genotypes (somatic embryos)

Macroecology of global alpine vegetation

Gli ecosistemi alpini, ossia gli habitat di alta quota al di sopra della linea degli alberi, sono essenziali per il sostentamento umano e sono tra gli ambienti più minacciati dal cambiamento climatico di origine antropica. Nonostante il consenso generale sulla distribuzione e le caratteristiche ecologiche dei biomi terrestri, l'effettiva estensione e le caratteristiche bioclimatiche degli ecosistemi alpini globali sono ancora incerte. Inoltre, i pattern e le cause della diversità vegetale e del funzionamento degli ecosistemi alpini globali sono in gran parte sconosciuti. Questo lavoro rappresenta un punto di partenza per la delineazione dei pattern macroecologici dei biomi alpini globali. In primo luogo, ho creato una mappa delle aree alpine globali modellando le quote altimetriche regionali della linea degli alberi ad alta risoluzione spaziale, utilizzando dataset globali di copertura forestale. Ho usato questa mappa in combinazione con altri dataset digitali per valutare le caratteristiche climatiche degli ecosistemi alpini e determinarne i pattern di produttività primaria. In secondo luogo, ho analizzato i pattern globali di ricchezza delle specie vegetali negli ecosistemi alpini e l’influenza di fattori ambientali, geografici e storici a diverse scale spaziali. Per fare ciò, ho messo insieme un dataset globale della vegetazione alpina composto da oltre 8.900 plot, ho valutato i pattern latitudinali di ricchezza regionale e a livello di singole comunità vegetali, e li ho modellati rispetto a diversi predittori, stimati utilizzando raster globali. Infine, ho analizzato la variazione funzionale della vegetazione alpina in rapporto alla storia evolutiva e al macroclima. Per fare ciò, ho ulteriormente selezionato il suddetto dataset di plot di vegetazione alpina in base alla disponibilità di tratti funzionali e dati filogenetici. Ho valutato le strategie funzionali delle diverse specie di piante alpine e la dissimilarità funzionale della vegetazione tra grandi unità geografiche caratterizzate da diversa vegetazione planiziale dominante, macroclima e storia evolutiva. Infine, ho modellato la dissimilarità funzionale rispetto alle dissimilarità ambientale e filogenetica. Dalle analisi effettuate, è emerso che i biomi alpini coprono quasi il 3% delle terre emerse al di fuori dell'Antartide. Nonostante le differenze di temperatura tra le diverse latitudini, questi ecosistemi convergono al di sotto di una soglia di 5,9 °C di temperatura media annua e verso l'estremità più fredda dello spazio climatico globale. Al di sotto di tale soglia di temperatura, gli ecosistemi alpini sono influenzati da un gradiente latitudinale di temperatura media annua e sono differenziati dal punto di vista climatico per stagionalità e continentalità. Questo gradiente distingue lo spazio climatico dei biomi alpini globali da quello dei biomi temperati, boreali e della tundra. Sebbene i biomi alpini siano similmente caratterizzati da aree scarsamente vegetate, le ecoregioni mondiali mostrano forti differenze nella produttività della loro fascia alpina indipendentemente dalle principali zone climatiche. Inoltre, in contrasto con il ben noto gradiente di diversità latitudinale, la ricchezza di specie vegetali alpine di alcune regioni temperate dell'Eurasia è paragonabile a quella degli ecosistemi alpini tropicali. Questo pattern è principalmente spiegato dall'estensione attuale e passata delle aree alpine, dall'isolamento e dalla variazione del pH del suolo tra le diverse regioni, mentre la ricchezza delle comunità vegetali dipende da fattori ambientali locali. Infine, le specie vegetali delle aree alpine sembrano riflettere la variazione funzionale globale di tutte le piante e sono principalmente differenziate per le loro strategie di utilizzo delle risorse. Il macroclima attuale esercita un effetto limitato sulla vegetazione alpina, agendo per lo più a livello delle singole comunità vegetali e in combinazione con la storia evolutiva. Inoltre, la vegetazione alpina globale è funzionalmente indipendente dalle zone di vegetazione in cui è integrata, mostrando una forte convergenza funzionale. Nel complesso, nonostante la loro distribuzione globale e l'apparente eterogeneità, gli ambienti alpini formano un gruppo distinto di biomi funzionalmente convergenti, fortemente disaccoppiati dagli ambienti di pianura e con una storia biogeografica varia, la cui eredità può ancora essere osservata sugli attuali pattern di diversità che sono ulteriormente rifiniti da fattori locali.Alpine ecosystems, namely high-elevation habitats above the climatic treeline, are essential to human livelihoods and are among the environments with the highest vulnerability to anthropogenic climate change. Despite the overall agreement on the distribution and ecological features of terrestrial biomes, the actual extent and bioclimatic characteristics of alpine ecosystems worldwide are still uncertain. Furthermore, the patterns and drivers of plant diversity and functioning in alpine ecosystems are largely unknown at the global scale. This work represents a novel contribution to the delineation of macroecological patterns of global alpine biomes. First, I created a map of global alpine areas by modelling regional treeline elevations at high spatial resolution using global forest cover data. I used this map in combination with global digital datasets to assess the climatic characteristics of alpine ecosystems and to evaluate patterns of primary productivity. Second, I assessed the global patterns of plant species richness in alpine ecosystems and the relative effect of environmental, geographical and historical factors at different spatial scales. To do so, I compiled a global dataset of alpine vegetation consisting of more than 8,900 plots, evaluated latitudinal patterns of regional and community richness and modelled them against different predictors estimated using global raster layers. Third, I assessed the functional variation of alpine vegetation and its relationship with evolutionary history and macroclimate. I filtered the abovementioned dataset of alpine vegetation plots based on the availability of functional trait and phylogenetic data. I assessed the functional trade-offs of alpine plant species and the functional dissimilarity of alpine vegetation across large geographic units with different dominant lowland vegetation, macroclimate, and evolutionary history. Finally, I modelled functional dissimilarity against environmental and phylogenetic dissimilarity. I found that alpine biomes cover almost 3% of land outside Antarctica. Despite temperature differences across latitudes, these ecosystems converge below a sharp threshold of 5.9 °C and towards the colder end of the global climatic space. Below that temperature threshold, alpine ecosystems are influenced by a latitudinal gradient of mean annual temperature and are climatically differentiated by seasonality and continentality. This gradient delineates a climatic envelope of global alpine biomes. Although alpine biomes are similarly dominated by poorly vegetated areas, world ecoregions show strong differences in the productivity of their alpine belt irrespectively of major climate zones. Furthermore, in contrast with the well-known latitudinal diversity gradient, plant species richness of some temperate alpine regions in Eurasia is comparable to that of hyper-diverse tropical alpine ecosystems. This pattern is mainly explained by the current and past alpine area, isolation, and variation in soil pH among regions, while community richness depends on local environmental factors. Finally, plant species in alpine areas seemingly reflect the global variation of plant function and are mainly differentiated for their resource-use strategies. The current macroclimate exerts a limited effect on alpine vegetation, mostly acting at the community level in combination with evolutionary history. Alpine vegetation is also functionally independent from the vegetation zones in which it is embedded, exhibiting strong functional convergence at the global scale. Overall, despite their global distribution and apparent heterogeneity, alpine environments form a distinct group of functionally convergent biomes, strongly decoupled from lowland environments, and with a varied biogeographic history, whose legacy can still be observed on current diversity patterns which are locally refined by fine-scale factors

The Challenge of Modeling the Acquisition of Mathematical Concepts

As a full-blown research topic, numerical cognition is investigated by a variety of disciplines including cognitive science, developmental and educational psychology, linguistics, anthropology and, more recently, biology and neuroscience. However, despite the great progress achieved by such a broad and diversified scientific inquiry, we are still lacking a comprehensive theory that could explain how numerical concepts are learned by the human brain. In this perspective, I argue that computer simulation should have a primary role in filling this gap because it allows identifying the finer-grained computational mechanisms underlying complex behavior and cognition. Modeling efforts will be most effective if carried out at cross-disciplinary intersections, as attested by the recent success in simulating human cognition using techniques developed in the fields of artificial intelligence and machine learning. In this respect, deep learning models have provided valuable insights into our most basic quantification abilities, showing how numerosity perception could emerge in multi-layered neural networks that learn the statistical structure of their visual environment. Nevertheless, this modeling approach has not yet scaled to more sophisticated cognitive skills that are foundational to higher-level mathematical thinking, such as those involving the use of symbolic numbers and arithmetic principles. I will discuss promising directions to push deep learning into this uncharted territory. If successful, such endeavor would allow simulating the acquisition of numerical concepts in its full complexity, guiding empirical investigation on the richest soil and possibly offering far-reaching implications for educational practice

Can neural networks do arithmetic? A survey on the elementary numerical skills of state-of-the-art deep learning models

Creating learning models that can exhibit sophisticated reasoning skills is one of the greatest challenges in deep learning research, and mathematics is rapidly becoming one of the target domains for assessing scientific progress in this direction. In the past few years there has been an explosion of neural network architectures, data sets, and benchmarks specifically designed to tackle mathematical problems, reporting notable success in disparate fields such as automated theorem proving, numerical integration, and discovery of new conjectures or matrix multiplication algorithms. However, despite these impressive achievements it is still unclear whether deep learning models possess an elementary understanding of quantities and symbolic numbers. In this survey we critically examine the recent literature, concluding that even state-of-the-art architectures often fall short when probed with relatively simple tasks designed to test basic numerical and arithmetic knowledge

Operação Mandacaru: descobrindo oportunidades e melhorando a qualidade de vida

Trabalho apresentado no II Congresso Nacional do PROJETO RONDON, realizado em Florianópolis, SC, no período de 23 a 25 de setembro de 2015 - Universidade Federal de Santa Catarina.A Operação Mandacaru do Projeto Rondon foi realizada no Estado do Ceará, região nordeste do país, de 17 a 31 de janeiro de 2015. A UTFPR, Câmpus Pato Branco participou da operação trabalhando com o conjunto B na cidade de Itapiúna. O Município localizado no início do sertão cearense, apresenta uma população de aproximadamente 20 mil habitantes. Uma região muito carente de recursos, que sofre com a falta de chuvas. Com o intuito de mostrar oportunidades para melhorar as condições de vida da população foram desenvolvidas oficinas de Empreendedorismo, Associativismo, Elaboração de Projetos para Captação de Recursos e a pedido da administração municipal uma palestra sobre o lixo, tamanha era a quantidade deste espalhada, tanto na cidade quanto no interior. A oficina de Associativismo foi trabalhada com o objetivo de estimular a organização, formalização e profissionalização de organizações associativas, abordando tópicos como o que é o Associativismo, para que serve, como funciona, quais os princípios e quais os benefícios que uma associação pode trazer para a sociedade, além da realização de uma dinâmica de identificação de formas de associação que poderiam ser criadas na comunidade local. Já, a oficina de Empreendedorismo foi ministrada com o objetivo de sensibilizar os participantes para ações empreendedoras. Foram abordados o conceito de empreendedorismo, perfil do empreendedor, levantamento de potencialidades e geração de ideias. A oficina de Elaboração de Projetos para Captação de Recursos foi desenvolvida com a finalidade de capacitar os gestores municipais na elaboração de projetos para captação recursos junto ao governo federal. A palestra do lixo teve como propósito conscientizar as pessoas a respeito dos perigos causados pelo lixo quando este não é descartado de forma correta. Visando estimular a comunidade a jogar o lixo no lixo, e não em qualquer lugar, foi desenvolvido um mutirão para recolher um pouco do lixo nas ruas. Para todos os trabalhos foram utilizados recursos como apresentações de power point e dinâmicas de grupos. No decorrer do desenvolvimento das oficinas pode-se observar a participação de pessoas de todas as idades, muitos com a curiosidade, por exemplo, de saber o que era uma oficina e o que era o Empreendedorismo. Isso por si só já valeria todo o esforço de qualquer rondonista. A população é carente de recursos, mas mais carente ainda de carinho e atenção e isso é demonstrado na participação e no envolvimento da comunidade. O rondonista ao mesmo tempo que ensina, aprende muito mais

Quality of Living Perceived of the Young People of the Vale do Sinos / RS/Brazil

The objective of this study was to identify the Quality of Life level of young people from Vale do Sinos / RS. The methodology was observational, descriptive and transversal. The sample had 391 young people. The data collection instruments were the WHOQOL-Bref and a socioeconomic questionnaire. Resulting in a total average score of 52.37 points, the highest for the Social Relations Domain and the lowest for the Environment. When compared to the variables "Sex", "Income" and "Age", the one with the highest statistically significant association among the domains was "Income", followed by "Sex" and "Age". It was concluded that the QoL of young people is smaller when compared with other studies, being relevant investments in public policies