A walk in the statistical mechanical formulation of neural networks

Neural networks are nowadays both powerful operational tools (e.g., for pattern recognition, data mining, error correction codes) and complex theoretical models on the focus of scientific investigation. As for the research branch, neural networks are handled and studied by psychologists, neurobiologists, engineers, mathematicians and theoretical physicists. In particular, in theoretical physics, the key instrument for the quantitative analysis of neural networks is statistical mechanics. From this perspective, here, we first review attractor networks: starting from ferromagnets and spin-glass models, we discuss the underlying philosophy and we recover the strand paved by Hopfield, Amit-Gutfreund-Sompolinky. One step forward, we highlight the structural equivalence between Hopfield networks (modeling retrieval) and Boltzmann machines (modeling learning), hence realizing a deep bridge linking two inseparable aspects of biological and robotic spontaneous cognition. As a sideline, in this walk we derive two alternative (with respect to the original Hebb proposal) ways to recover the Hebbian paradigm, stemming from ferromagnets and from spin-glasses, respectively. Further, as these notes are thought of for an Engineering audience, we highlight also the mappings between ferromagnets and operational amplifiers and between antiferromagnets and flip-flops (as neural networks -built by op-amp and flip-flops- are particular spin-glasses and the latter are indeed combinations of ferromagnets and antiferromagnets), hoping that such a bridge plays as a concrete prescription to capture the beauty of robotics from the statistical mechanical perspective.Comment: Contribute to the proceeding of the conference: NCTA 2014. Contains 12 pages,7 figure

Unsupervised Recognition of Multi-Resident Activities in Smart-Homes

Several methods have been proposed in the last two decades to recognize human activities based on sensor data acquired in smart-homes. While most existing methods assume the presence of a single inhabitant, a few techniques tackle the challenging issue of multi-resident activity recognition. To the best of our knowledge, all existing methods for multi-inhabitant activity recognition require the acquisition of a labeled training set of activities and sensor events. Unfortunately, activity labeling is costly and may disrupt the users' privacy. In this article, we introduce a novel technique to recognize multi-inhabitant activities without the need of labeled datasets. Our technique relies on an unlabeled sensor data stream acquired from a single resident, and on ontological reasoning to extract probabilistic associations among sensor events and activities. Extensive experiments with a large dataset of multi-inhabitant activities show that our technique achieves an average accuracy very close to the one of state-of-the-art supervised methods, without requiring the acquisition of labeled data

Mapping and classification of ecologically sensitive marine habitats using unmanned aerial vehicle (UAV) imagery and object-based image analysis (OBIA)

Nowadays, emerging technologies, such as long-range transmitters, increasingly miniaturized components for positioning, and enhanced imaging sensors, have led to an upsurge in the availability of new ecological applications for remote sensing based on unmanned aerial vehicles (UAVs), sometimes referred to as “drones”. In fact, structure-from-motion (SfM) photogrammetry coupled with imagery acquired by UAVs offers a rapid and inexpensive tool to produce high-resolution orthomosaics, giving ecologists a new way for responsive, timely, and cost-effective monitoring of ecological processes. Here, we adopted a lightweight quadcopter as an aerial survey tool and object-based image analysis (OBIA) workflow to demonstrate the strength of such methods in producing very high spatial resolution maps of sensitive marine habitats. Therefore, three different coastal environments were mapped using the autonomous flight capability of a lightweight UAV equipped with a fully stabilized consumer-grade RGB digital camera. In particular we investigated a Posidonia oceanica seagrass meadow, a rocky coast with nurseries for juvenile fish, and two sandy areas showing biogenic reefs of Sabelleria alveolata. We adopted, for the first time, UAV-based raster thematic maps of these key coastal habitats, produced after OBIA classification, as a new method for fine-scale, low-cost, and time saving characterization of sensitive marine environments which may lead to a more effective and efficient monitoring and management of natural resource

Meta-stable states in the hierarchical Dyson model drive parallel processing in the hierarchical Hopfield network

In this paper we introduce and investigate the statistical mechanics of hierarchical neural networks: First, we approach these systems \`a la Mattis, by thinking at the Dyson model as a single-pattern hierarchical neural network and we discuss the stability of different retrievable states as predicted by the related self-consistencies obtained from a mean-field bound and from a bound that bypasses the mean-field limitation. The latter is worked out by properly reabsorbing fluctuations of the magnetization related to higher levels of the hierarchy into effective fields for the lower levels. Remarkably, mixing Amit's ansatz technique (to select candidate retrievable states) with the interpolation procedure (to solve for the free energy of these states) we prove that (due to gauge symmetry) the Dyson model accomplishes both serial and parallel processing. One step forward, we extend this scenario toward multiple stored patterns by implementing the Hebb prescription for learning within the couplings. This results in an Hopfield-like networks constrained on a hierarchical topology, for which, restricting to the low storage regime (where the number of patterns grows at most logarithmical with the amount of neurons), we prove the existence of the thermodynamic limit for the free energy and we give an explicit expression of its mean field bound and of the related improved boun

Topological properties of hierarchical networks

Hierarchical networks are attracting a renewal interest for modelling the organization of a number of biological systems and for tackling the complexity of statistical mechanical models beyond mean-field limitations. Here we consider the Dyson hierarchical construction for ferromagnets, neural networks and spin-glasses, recently analyzed from a statistical-mechanics perspective, and we focus on the topological properties of the underlying structures. In particular, we find that such structures are weighted graphs that exhibit high degree of clustering and of modularity, with small spectral gap; the robustness of such features with respect to link removal is also studied. These outcomes are then discussed and related to the statistical mechanics scenario in full consistency. Lastly, we look at these weighted graphs as Markov chains and we show that in the limit of infinite size, the emergence of ergodicity breakdown for the stochastic process mirrors the emergence of meta-stabilities in the corresponding statistical mechanical analysis

Hierarchical neural networks perform both serial and parallel processing

In this work we study a Hebbian neural network, where neurons are arranged according to a hierarchical architecture such that their couplings scale with their reciprocal distance. As a full statistical mechanics solution is not yet available, after a streamlined introduction to the state of the art via that route, the problem is consistently approached through signal- to-noise technique and extensive numerical simulations. Focusing on the low-storage regime, where the amount of stored patterns grows at most logarithmical with the system size, we prove that these non-mean-field Hopfield-like networks display a richer phase diagram than their classical counterparts. In particular, these networks are able to perform serial processing (i.e. retrieve one pattern at a time through a complete rearrangement of the whole ensemble of neurons) as well as parallel processing (i.e. retrieve several patterns simultaneously, delegating the management of diff erent patterns to diverse communities that build network). The tune between the two regimes is given by the rate of the coupling decay and by the level of noise affecting the system. The price to pay for those remarkable capabilities lies in a network's capacity smaller than the mean field counterpart, thus yielding a new budget principle: the wider the multitasking capabilities, the lower the network load and viceversa. This may have important implications in our understanding of biological complexity

From Dyson to Hopfield: Processing on hierarchical networks

We consider statistical-mechanical models for spin systems built on hierarchical structures, which provide a simple example of non-mean-field framework. We show that the coupling decay with spin distance can give rise to peculiar features and phase diagrams much richer that their mean-field counterpart. In particular, we consider the Dyson model, mimicking ferromagnetism in lattices, and we prove the existence of a number of meta-stabilities, beyond the ordered state, which get stable in the thermodynamic limit. Such a feature is retained when the hierarchical structure is coupled with the Hebb rule for learning, hence mimicking the modular architecture of neurons, and gives rise to an associative network able to perform both as a serial processor as well as a parallel processor, depending crucially on the external stimuli and on the rate of interaction decay with distance; however, those emergent multitasking features reduce the network capacity with respect to the mean-field counterpart. The analysis is accomplished through statistical mechanics, graph theory, signal-to-noise technique and numerical simulations in full consistency. Our results shed light on the biological complexity shown by real networks, and suggest future directions for understanding more realistic models

Hormônio do crescimento e exercício físico

O exercício físico é um potente estímulo fisiológico para a secreção de GH, e tanto o exercício aeróbio quanto o exercício de força são capazes de aumentar a sua secreção. Entretanto, a idade, o sexo, o nível de aptidão física e o percentual de gordura são fatores que interferem na secreção de GH em resposta ao exercício. Existe uma relação linear entre intensidade do exercício aeróbio e magnitude de aumento do GH. Em relação ao exercício de força, é importante se controlar o tempo de intervalo entre as séries, a carga e a frequencia da sessão de treino para que ocorra aumento de GH. O eixo GH/IGF-1 exerce efeitos metabólicos a curto e a longo prazo que são importantes durante e após o exercício, como por exemplo a regulação do metabolismo de substratos, favorecendo a mobilização de ácidos graxos livres do tecido adiposo para a geração de energia, aumentando a oxidação da gordura e o gasto energético. O exercício regular também pode aumentar a taxa de secreção de GH durante 24h, contribuindo para as adaptações ao treinamento. Os efeitos da reposição de GH em pessoas que apresentam deficiência deste hormônio mostram diversos efeitos relacionados a melhora da composição corporal e capacidade física, porém nem sempre esses efeitos podem ser vistos em sujeitos saudáveis e muito menos traduzidos em performance física. Mesmo assim, o abuso de GH no meio esportivo profissional e por entusiastas da atividade física não é algo incomum. Sendo assim, esta revisão pretende mostrar: 1) as evidências sobre a influência do exercício físico aeróbio e de força, tanto agudo quanto crônico, na secreção de GH; 2) descrever os mecanismos que regulam a secreção fisiológica de GH e que podem influenciar na resposta da secreção do GH ao exercício; 3) abordar os efeitos do GH no metabolismo em repouso e durante o exercício e 4) entender os motivos que justificam seu uso e abuso por atletas e entusiastas da atividade física

Unmanned Aerial Systems (UASs) for Environmental Monitoring: A Review with Applications in Coastal Habitats

Nowadays the proliferation of small unmanned aerial systems or vehicles (UAS/Vs), formerly known as drones, coupled with an increasing interest in tools for environmental monitoring, have led to an exponential use of these unmanned aerial platforms for many applications in the most diverse fields of science. In particular, ecologists require data collected at appropriate spatial and temporal resolutions to describe ecological processes. For these reasons, we are witnessing the proliferation of UAV-based remote sensing techniques because they provide new perspectives on ecological phenomena that would otherwise be difficult to study. Therefore, we propose a brief review regarding the emerging applications of low-cost aerial platforms in the field of environmental sciences such as assessment of vegetation dynamics and forests biodiversity, wildlife research and management, map changes in freshwater marshes, river habitat mapping, and conservation and monitoring programs. In addition, we describe two applications of habitat mapping from UAS-based imagery, along the Central Mediterranean coasts, as study cases: (1) The upper limit of a Posidonia oceanica meadow was mapped to detect impacted areas, (2) high-resolution orthomosaic was used for supporting underwater visual census data in order to visualize juvenile fish densities and microhabitat use in four shallow coastal nurseries

Nationwide Survey of Healthcare Services for Autism Spectrum Disorders (ASD) in Italy

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