scholarly journals Hardware Design for Autonomous Bayesian Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Rafatul Faria ◽  
Jan Kaiser ◽  
Kerem Y. Camsari ◽  
Supriyo Datta
Keyword(s):  
Bayesian Networks ◽  
Causal Reasoning ◽  
Large Scale ◽  
Behavioral Model ◽  
Building Blocks ◽  
Directed Acyclic Graphs ◽  
Parallel Operation ◽  
Probabilistic Circuits ◽  
Large Scale Networks ◽  
The Individual

Directed acyclic graphs or Bayesian networks that are popular in many AI-related sectors for probabilistic inference and causal reasoning can be mapped to probabilistic circuits built out of probabilistic bits (p-bits), analogous to binary stochastic neurons of stochastic artificial neural networks. In order to satisfy standard statistical results, individual p-bits not only need to be updated sequentially but also in order from the parent to the child nodes, necessitating the use of sequencers in software implementations. In this article, we first use SPICE simulations to show that an autonomous hardware Bayesian network can operate correctly without any clocks or sequencers, but only if the individual p-bits are appropriately designed. We then present a simple behavioral model of the autonomous hardware illustrating the essential characteristics needed for correct sequencer-free operation. This model is also benchmarked against SPICE simulations and can be used to simulate large-scale networks. Our results could be useful in the design of hardware accelerators that use energy-efficient building blocks suited for low-level implementations of Bayesian networks. The autonomous massively parallel operation of our proposed stochastic hardware has biological relevance since neural dynamics in brain is also stochastic and autonomous by nature.

scholarly journals Time-resolved connectome of the five-factor model of personality

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
L. Passamonti ◽  
R. Riccelli ◽  
I. Indovina ◽  
A. Duggento ◽  
A. Terracciano ◽  
...  
Keyword(s):  
Large Scale ◽  
Ad Hoc ◽  
Factor Model ◽  
Five Factor Model ◽  
Future Research ◽  
Time Resolved ◽  
Personality Differences ◽  
Network Time ◽  
Large Scale Networks ◽  
The Individual

Abstract The human brain is characterized by highly dynamic patterns of functional connectivity. However, it is unknown whether this time-variant ‘connectome’ is related to the individual differences in the behavioural and cognitive traits described in the five-factor model of personality. To answer this question, inter-network time-variant connectivity was computed in n = 818 healthy people via a dynamical conditional correlation model. Next, network dynamicity was quantified throughout an ad-hoc measure (T-index) and the generalizability of the multi-variate associations between personality traits and network dynamicity was assessed using a train/test split approach. Conscientiousness, reflecting enhanced cognitive and emotional control, was the sole trait linked to stationary connectivity across several circuits such as the default mode and prefronto-parietal network. The stationarity in the ‘communication’ across large-scale networks offers a mechanistic description of the capacity of conscientious people to ‘protect’ non-immediate goals against interference over-time. This study informs future research aiming at developing more realistic models of the brain dynamics mediating personality differences.

Building large-scale Bayesian networks

2000 ◽  
Vol 15 (3) ◽  
pp. 257-284 ◽  
Author(s):  
MARTIN NEIL ◽  
NORMAN FENTON ◽  
LARS NIELSON
Keyword(s):  
Bayesian Networks ◽  
Conditional Probability ◽  
Large Scale ◽  
Cost Effective ◽  
Building Blocks ◽  
Software Safety ◽  
Combination Rules ◽  
Uncertain Variables ◽  
Software Engineers ◽  
Simple Combination

Bayesian networks (BNs) model problems that involve uncertainty. A BN is a directed graph, whose nodes are the uncertain variables and whose edges are the causal or influential links between the variables. Associated with each node is a set of conditional probability functions that model the uncertain relationship between the node and its parents. The benefits of using BNs to model uncertain domains are well known, especially since the recent breakthroughs in algorithms and tools to implement them. However, there have been serious problems for practitioners trying to use BNs to solve realistic problems. This is because, although the tools make it possible to execute large-scale BNs efficiently, there have been no guidelines on building BNs. Specifically, practitioners face two significant barriers. The first barrier is that of specifying the graph structure such that it is a sensible model of the types of reasoning being applied. The second barrier is that of eliciting the conditional probability values. In this paper we concentrate on this first problem. Our solution is based on the notion of generally applicable “building blocks”, called idioms, which serve solution patterns. These can then in turn be combined into larger BNs, using simple combination rules and by exploiting recent ideas on modular and object oriented BNs (OOBNs). This approach, which has been implemented in a BN tool, can be applied in many problem domains. We use examples to illustrate how it has been applied to build large-scale BNs for predicting software safety. In the paper we review related research from the knowledge and software engineering literature. This provides some context to the work and supports our argument that BN knowledge engineers require the same types of processes, methods and strategies enjoyed by systems and software engineers if they are to succeed in producing timely, quality and cost-effective BN decision support solutions.

scholarly journals Hardware implementation of Bayesian network building blocks with stochastic spintronic devices

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Punyashloka Debashis ◽  
Vaibhav Ostwal ◽  
Rafatul Faria ◽  
Supriyo Datta ◽  
Joerg Appenzeller ◽  
...  
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Large Scale ◽  
Hardware Implementation ◽  
Reducing Power ◽  
Perpendicular Magnetic Anisotropy ◽  
Building Blocks ◽  
Spintronic Devices ◽  
Network Building ◽  
Hardware Implementations

Abstract Bayesian networks are powerful statistical models to understand causal relationships in real-world probabilistic problems such as diagnosis, forecasting, computer vision, etc. For systems that involve complex causal dependencies among many variables, the complexity of the associated Bayesian networks become computationally intractable. As a result, direct hardware implementation of these networks is one promising approach to reducing power consumption and execution time. However, the few hardware implementations of Bayesian networks presented in literature rely on deterministic CMOS devices that are not efficient in representing the stochastic variables in a Bayesian network that encode the probability of occurrence of the associated event. This work presents an experimental demonstration of a Bayesian network building block implemented with inherently stochastic spintronic devices based on the natural physics of nanomagnets. These devices are based on nanomagnets with perpendicular magnetic anisotropy, initialized to their hard axes by the spin orbit torque from a heavy metal under-layer utilizing the giant spin Hall effect, enabling stochastic behavior. We construct an electrically interconnected network of two stochastic devices and manipulate the correlations between their states by changing connection weights and biases. By mapping given conditional probability tables to the circuit hardware, we demonstrate that any two node Bayesian networks can be implemented by our stochastic network. We then present the stochastic simulation of an example case of a four node Bayesian network using our proposed device, with parameters taken from the experiment. We view this work as a first step towards the large scale hardware implementation of Bayesian networks.

scholarly journals Interacting Urns Processes for Clustering of Large-Scale Networks of Tiny Artifacts

2010 ◽  
Vol 6 (1) ◽  
pp. 936195 ◽  
Author(s):  
Pierre Leone ◽  
Elad M. Schiller
Keyword(s):  
Random Process ◽  
Large Scale ◽  
Numerical Studies ◽  
Random Ratio ◽  
Communication Graphs ◽  
Global Topology ◽  
Theoretical Predictions ◽  
Large Scale Networks ◽  
The Individual ◽  
Colored Ball

We analyze a distributed variation on the Pólya urn process in which a network of tiny artifacts manages the individual urns. Neighboring urns interact by repeatedly adding the same colored ball based on previous random choices. We discover that the process rapidly converges to a definitive random ratio between the colors in every urn. Moreover, the rate of convergence of the process at a given node depends on the global topology of the network. In particular, the same ratio appears for the case of complete communication graphs. Surprisingly, this effortless random process supports useful applications, such as clustering and computation of pseudo-geometric coordinate. We present numerical studies that validate our theoretical predictions.

scholarly journals EPIDEMIC INDIVIDUAL-BASED MODELS APPLIED IN RANDOM AND SCALE-FREE NETWORKS

2020 ◽  
Vol 38 (1) ◽  
pp. 102
Author(s):  
Christofer Roque Ribeiro SILVA ◽  
Alexandre Celestino Leite ALMEIDA ◽  
Rodrigo Tomás Nogueira CARDOSO ◽  
Ricardo Hiroshi Caldeira TAKAHASHI
Keyword(s):  
Large Scale ◽  
Deterministic Model ◽  
Statistical Tests ◽  
Network Models ◽  
Scale Free ◽  
Individual Based Models ◽  
Network Topologies ◽  
Large Scale Networks ◽  
The Individual

This work proposes a version of the Individual-Based Model (IBM) that converges, on average, to the result of the SIR (Susceptible-Infected-Recovered) model, and studies the effect of this IBM in two types of networks: random and scale-free. A numerical computational case study is considered, using large scale networks implemented by an efficient framework. Statistical tests are performed to show the similarities and differences between the network models and the deterministic model taken as a baseline. Simulation results verify that different network topologies alter the behavior of the epidemic propagation in the following aspects: temporal evolution, basal reproducibility and the number of infected in the final.

scholarly journals Short-term modulation of the lesioned language network

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Gesa Hartwigsen ◽  
Anika Stockert ◽  
Louise Charpentier ◽  
Max Wawrzyniak ◽  
Julian Klingbeil ◽  
...  
Keyword(s):  
Large Scale ◽  
Right Hemisphere ◽  
Functional Neuroimaging ◽  
Inferior Frontal Gyrus ◽  
Individual Response ◽  
Short Term ◽  
Large Scale Networks ◽  
The Individual ◽  
The Right ◽  
Language Network

Language is sustained by large-scale networks in the human brain. Stroke often severely affects function and network dynamics. However, the adaptive potential of the brain to compensate for lesions is poorly understood. A key question is whether upregulation of the right hemisphere is adaptive for language recovery. Targeting the potential for short-term reorganization in the lesioned brain, we applied 'virtual lesions' over left anterior or posterior inferior frontal gyrus (IFG) in post-stroke patients with left temporo-parietal lesions prior to functional neuroimaging. Perturbation of the posterior IFG selectively delayed phonological decisions and decreased phonological activity. The individual response delay was correlated with the upregulation of the lesion homologue, likely reflecting compensation. Moreover, stronger individual tract integrity of the right superior longitudinal fascicle was associated with lesser disruption. Our results provide evidence for functional and structural underpinnings of plasticity in the lesioned language network, and a compensatory role of the right hemisphere.

Models, Practices and Methods of Reading: Evolution in Time and Space

2009 ◽  
pp. 59-64
Author(s):  
Yulia P. Melentyeva
Keyword(s):  
Large Scale ◽  
Social Groups ◽  
National Program ◽  
Time And Space ◽  
Lack Of Information ◽  
The Individual

In recent years as public in general and specialist have been showing big interest to the matters of reading. According to discussion and launch of the “Support and Development of Reading National Program”, many Russian libraries are organizing the large-scale events like marathons, lecture cycles, bibliographic trainings etc. which should draw attention of different social groups to reading. The individual forms of attraction to reading are used much rare. To author’s mind the main reason of such an issue has to be the lack of information about forms and methods of attraction to reading.

Fully Automated Parallel Oligonucleotide Synthesizer

2001 ◽  
Vol 66 (8) ◽  
pp. 1299-1314 ◽  
Author(s):  
Michal Lebl ◽  
Christine Burger ◽  
Brett Ellman ◽  
David Heiner ◽  
Georges Ibrahim ◽  
...  
Keyword(s):  
Solid Phase ◽  
Building Blocks ◽  
Solid Support ◽  
Continuous Operation ◽  
Gear Pump ◽  
Oligonucleotide Synthesis ◽  
Center Of Rotation ◽  
Microtiter Plates ◽  
The Individual ◽  
Design And Construction

Design and construction of automated synthesizers using the tilted plate centrifugation technology is described. Wash solutions and reagents common to all synthesized species are delivered automatically through a 96-channel distributor connected to a gear pump through two four-port selector valves. Building blocks and other specific reagents are delivered automatically through banks of solenoid valves, positioned over the individual wells of the microtiterplate. These instruments have the following capabilities: Parallel solid-phase oligonucleotide synthesis in the wells of polypropylene microtiter plates, which are slightly tilted down towards the center of rotation, thus generating a pocket in each well, in which the solid support is collected during centrifugation, while the liquid is expelled from the wells. Eight microtiterplates are processed simultaneously, providing thus a synthesizer with a capacity of 768 parallel syntheses. The instruments are capable of unattended continuous operation, providing thus a capacity of over two millions 20-mer oligonucleotides in a year.

Biogas Production Using Anaerobic Digestion of Industrial Waste

2016 ◽  
Vol 832 ◽  
pp. 55-62
Author(s):  
Ján Gaduš ◽  
Tomáš Giertl ◽  
Viera Kažimírová
Keyword(s):  
Anaerobic Digestion ◽  
Industrial Waste ◽  
Large Scale ◽  
Biogas Production ◽  
Biogas Plant ◽  
Parallel Operation ◽  
Paper Production ◽  
Biochemical Conversion ◽  
Mixed Biomass ◽  
Economic Balance

In the paper experiments and theory of biogas production using industrial waste from paper production as a co-substrate are described. The main aim of the experiments was to evaluate the sensitivity and applicability of the biochemical conversion using the anaerobic digestion of the mixed biomass in the pilot fermentor (5 m3), where the mesophillic temperature was maintained. It was in parallel operation with a large scale fermentor (100 m3). The research was carried out at the biogas plant in Kolíňany, which is a demonstration facility of the Slovak University of Agriculture in Nitra. The experiments proved that the waste arising from the paper production can be used in case of its appropriate dosing as an input substrate for biogas production, and thus it can improve the economic balance of the biogas plant.

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