A Comparison of Algorithms for Learning Hidden Variables in Normal Graphs

A Bayesian factor graph reduced to normal form consists in the interconnection of diverter units (or equal constraint units) and Single-Input/Single-Output (SISO) blocks. In this framework localized adaptation rules are explicitly derived from a constrained maximum likelihood (ML) formulation and from a minimum KL-divergence criterion using KKT conditions. The learning algorithms are compared with two other updating equations based on a Viterbi-like and on a variational approximation respectively. The performance of the various algorithm is verified on synthetic data sets for various architectures. The objective of this paper is to provide the programmer with explicit algorithms for rapid deployment of Bayesian graphs in the applications.Comment: Submitted for journal publicatio

Towards Building Deep Networks with Bayesian Factor Graphs

We propose a Multi-Layer Network based on the Bayesian framework of the Factor Graphs in Reduced Normal Form (FGrn) applied to a two-dimensional lattice. The Latent Variable Model (LVM) is the basic building block of a quadtree hierarchy built on top of a bottom layer of random variables that represent pixels of an image, a feature map, or more generally a collection of spatially distributed discrete variables. The multi-layer architecture implements a hierarchical data representation that, via belief propagation, can be used for learning and inference. Typical uses are pattern completion, correction and classification. The FGrn paradigm provides great flexibility and modularity and appears as a promising candidate for building deep networks: the system can be easily extended by introducing new and different (in cardinality and in type) variables. Prior knowledge, or supervised information, can be introduced at different scales. The FGrn paradigm provides a handy way for building all kinds of architectures by interconnecting only three types of units: Single Input Single Output (SISO) blocks, Sources and Replicators. The network is designed like a circuit diagram and the belief messages flow bidirectionally in the whole system. The learning algorithms operate only locally within each block. The framework is demonstrated in this paper in a three-layer structure applied to images extracted from a standard data set.Comment: Submitted for journal publicatio

3-D Hand Pose Estimation from Kinect's Point Cloud Using Appearance Matching

We present a novel appearance-based approach for pose estimation of a human hand using the point clouds provided by the low-cost Microsoft Kinect sensor. Both the free-hand case, in which the hand is isolated from the surrounding environment, and the hand-object case, in which the different types of interactions are classified, have been considered. The hand-object case is clearly the most challenging task having to deal with multiple tracks. The approach proposed here belongs to the class of partial pose estimation where the estimated pose in a frame is used for the initialization of the next one. The pose estimation is obtained by applying a modified version of the Iterative Closest Point (ICP) algorithm to synthetic models to obtain the rigid transformation that aligns each model with respect to the input data. The proposed framework uses a "pure" point cloud as provided by the Kinect sensor without any other information such as RGB values or normal vector components. For this reason, the proposed method can also be applied to data obtained from other types of depth sensor, or RGB-D camera

“DALSTON! WHO ASKED U?”: A Knowledge-Centred Perspective on the Mapping of Socio-Spatial Relations in East London

Since the turn of the millennium, Dalston in the London Borough of Hackney has experienced fundamental change through public and private investment in new infrastructure and processes of urban restructuring. This was paralleled by the reform of the national planning system, which aimed to devolve decision-making to the local level and increase the possibilities for residents and stakeholders to participate in planning processes. However, the difficulty of translating local needs and aspirations into policy goals and broadly accepted area action plans resulted in a crisis, which, in 2018, led to the introduction of the Dalston Conversation and subsequently the revision of planning goals. It is in this context that the Relational States of Dalston mapping project generated and assembled local knowledge about the web of socio-spatial relations between different local actors and in this way highlighted the significance and fragility of the communities’ networks and their spatial dimensions. The collection, ordering, integration, and production of knowledge can be seen as part of the core work in urban planning processes and policymaking. Which forms of knowledge are routinely used in planning contexts and define the relationship between planning action and urban transformation? To what extent could the mapping of local community relations add to this knowledge and help to improve decision-making processes in contested spaces of knowledge? In what ways could a relational understanding of space and architectural modes of research and representation contribute to the analysis, conceptualisation, and communication of local community relations? This article engages with these questions, using the mapping project in Dalston as a case study

Self-adaptive decision-making mechanisms to balance the execution of multiple tasks for a multi-robots team

This work addresses the coordination problem of multiple robots with the goal of finding specific hazardous targets in an unknown area and dealing with them cooperatively. The desired behaviour for the robotic system entails multiple requirements, which may also be conflicting. The paper presents the problem as a constrained bi-objective optimization problem in which mobile robots must perform two specific tasks of exploration and at same time cooperation and coordination for disarming the hazardous targets. These objectives are opposed goals, in which one may be favored, but only at the expense of the other. Therefore, a good trade-off must be found. For this purpose, a nature-inspired approach and an analytical mathematical model to solve this problem considering a single equivalent weighted objective function are presented. The results of proposed coordination model, simulated in a two dimensional terrain, are showed in order to assess the behaviour of the proposed solution to tackle this problem. We have analyzed the performance of the approach and the influence of the weights of the objective function under different conditions: static and dynamic. In this latter situation, the robots may fail under the stringent limited budget of energy or for hazardous events. The paper concludes with a critical discussion of the experimental results

Swarm robotics in wireless distributed protocol design for coordinating robots involved in cooperative tasks

The mine detection in an unexplored area is an optimization problem where multiple mines, randomly distributed throughout an area, need to be discovered and disarmed in a minimum amount of time. We propose a strategy to explore an unknown area, using a stigmergy approach based on ants behavior, and a novel swarm based protocol to recruit and coordinate robots for disarming the mines cooperatively. Simulation tests are presented to show the effectiveness of our proposed Ant-based Task Robot Coordination (ATRC) with only the exploration task and with both exploration and recruiting strategies. Multiple minimization objectives have been considered: the robots' recruiting time and the overall area exploration time. We discuss, through simulation, different cases under different network and field conditions, performed by the robots. The results have shown that the proposed decentralized approaches enable the swarm of robots to perform cooperative tasks intelligently without any central control

Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption

Many applications, related to autonomous mobile robots, require to explore in an unknown environment searching for static targets, without any a priori information about the environment topology and target locations. Targets in such rescue missions can be fire, mines, human victims, or dangerous material that the robots have to handle. In these scenarios, some cooperation among the robots is required for accomplishing the mission. This paper focuses on the application of different bio-inspired metaheuristics for the coordination of a swarm of mobile robots that have to explore an unknown area in order to rescue and handle cooperatively some distributed targets. This problem is formulated by first defining an optimization model and then considering two sub-problems: exploration and recruiting. Firstly, the environment is incrementally explored by robots using a modified version of ant colony optimization. Then, when a robot detects a target, a recruiting mechanism is carried out to recruit a certain number of robots to deal with the found target together. For this latter purpose, we have proposed and compared three approaches based on three different bio-inspired algorithms (Firefly Algorithm, Particle Swarm Optimization, and Artificial Bee Algorithm). A computational study and extensive simulations have been carried out to assess the behavior of the proposed approaches and to analyze their performance in terms of total energy consumed by the robots to complete the mission. Simulation results indicate that the firefly-based strategy usually provides superior performance and can reduce the wastage of energy, especially in complex scenarios

Profiling Metacognition in Binge Eating Disorder

© 2020, The Author(s). Research has shown that metacognition may play a role in problem eating. In this study we explored whether aspects of metacognition are relevant to the understanding of binge eating in patients with Binge Eating Disorder. We aimed to ascertain: (1) the presence of metacognitive beliefs about binge eating; (2) the goal of, and stop signal for, binge eating; and (3) the impact of binge eating on self-consciousness. Ten Binge Eating Disorder patients took part in the study and were assessed using the metacognitive profiling semi-structured interview. Results suggested that all patients endorsed both positive and negative metacognitive beliefs about binge eating. The goals of binge eating were stop thinking about personal concerns and improve emotional state. All patients reported that they did not know when these goals had been reached. The stop signals for binge eating included physical discomfort, beliefs about binge eating not being the best way to solve problems, and environmental stimuli. All patients also confirmed that a reduction in self-consciousness occurred during a binge eating episode. The results of this study confirm that metacognition may indeed be relevant to the understanding of Binge Eating Disorder

Building a privacy-preserving semantic overlay for Peer-to-Peer networks

Searching a Peer-to-Peer (P2P) network without using a central index has been widely investigated but proved to be very difficult. Various strategies have been proposed, however no practical solution to date also addresses privacy concerns. By clustering peers which have similar interests, a semantic overlay provides a method for achieving scalable search. Traditionally, in order to find similar peers, a peer is required to fully expose its preferences for items or content, therefore disclosing this private information. However, in a hostile environment, such as a P2P system, a peer can not know the true identity or intentions of fellow peers. In this paper, we propose two protocols for building a semantic overlay in a privacy-preserving manner by modifying existing solutions to the Private Set Intersection (PSI) problem. Peers in our overlay compute their similarity to other peers in the encrypted domain, allowing them to find similar peers. Using homomorphic encryption, peers can carrying out computations on encrypted values, without needing to decrypt them first. We propose two protocols, one based on the inner product of vectors, the other on multivariate polynomial evaluation, which are able to compute a similarity value between two peers. Both protocols are implemented on top of an existing P2P platform and are designed for actual deployment. Using a supercomputer and a dataset extracted from a real world instance of a semantic overlay, we emulate our protocols in a network consisting of a thousand peers. Finally, we show the actual computational and bandwidth usage of the protocols as recorded during those experiments

The cleavable presequence is not essential for import and assembly of the phosphate carrier of mammalian mitochondria but enhances the specificity and efficiency of import.

The phosphate carrier (PiC) of mammalian mitochondria is synthesized with a cleavable presequence, in contrast to other members of the mitochondrial family of inner membrane carrier proteins. The precursor of PiC is efficiently imported, proteolytically processed, and correctly assembled in isolated mitochondria. Here we report that a presequence-deficient PiC was imported with an efficiency of about 50% as compared with the authentic precursor of PiC. This mature-sized PiC was correctly assembled, demonstrating that the presequence is not essential for the assembly pathway. We found the following functions for the PiC presequence. (i) The presequence by itself was able to target a passenger protein to mitochondria with a low efficiency, suggesting that the mammalian PiC contains multiple targeting signals, the more efficient one(s) present in the mature protein part. (ii) Deletion of the presequence allowed a more efficient heterologous import of mammalian PiC into mitochondria from Saccharomyces cerevisiae and Neurospora crassa, indicating an important role of the presequence in determining the specificity of PiC import. (iii) Import of the presequence-deficient PiC required a higher membrane potential across the inner membrane than that of the presequence-carrying form. Therefore, the presequence also enhances the translocation of PiC into the inner membrane