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 Bioenzymatic and Chemical Derivatization of Renewable Fatty Acids

Biomolecules ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 566 ◽  
Author(s):  
Akula ◽  
Kwon
Keyword(s):  
Large Scale ◽  
Dicarboxylic Acids ◽  
Cost Effective ◽  
Building Blocks ◽  
Synthetic Methods ◽  
Aqueous Alcohol ◽  
Chemical Transformations ◽  
Hydroxycarboxylic Acids ◽  
Escherichia Coli Cells ◽  
Catalyzed Oxidation

In addition to our previous efforts toward bioenzymatic and chemical transformations of ricinoleic acid and oleic acid to their corresponding ,-dicarboxylic acids via their ester intermediates driven in Escherichia coli cells, several efficient oxidation conditions were investigated and optimized for the conversion of -hydroxycarboxylic acids to ,-dicarboxylic acids. Pd/C-catalyzed oxidation using NaBH4 in a basic aqueous alcohol and Ni(II) salt-catalyzed oxidation using aqueous sodium hypochlorite were considered to be excellent as a hybrid reaction for three successive chemical reactions (hydrogenation, hydrolysis, and oxidation) and an eco-friendly, cost-effective, and practical approach, respectively. Omega-hydroxycarboxylic acids and -aminocarboxylic acid were also easily prepared as useful building blocks for plastics or bioactive compounds from the bioenzymatically driven ester intermediate. The scope of the developed synthetic methods can be utilized for large-scale synthesis and various derivatizations.

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 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.

Efficient Schedule of Energy-Constrained UAV Using Crowdsourced Buses in Last-Mile Parcel Delivery

2021 ◽  
Vol 5 (1) ◽  
pp. 1-23
Author(s):  
Yan Pan ◽  
Shining Li ◽  
Qianwu Chen ◽  
Nan Zhang ◽  
Tao Cheng ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Large Scale ◽  
Load Capacity ◽  
Scheduling Algorithm ◽  
Cost Effective ◽  
Promising Alternative ◽  
Logistics Industry ◽  
Last Mile ◽  
Delivery Scheduling ◽  
Schedule Approach

Stimulated by the dramatical service demand in the logistics industry, logistics trucks employed in last-mile parcel delivery bring critical public concerns, such as heavy cost burden, traffic congestion and air pollution. Unmanned Aerial Vehicles (UAVs) are a promising alternative tool in last-mile delivery, which is however limited by insufficient flight range and load capacity. This paper presents an innovative energy-limited logistics UAV schedule approach using crowdsourced buses. Specifically, when one UAV delivers a parcel, it first lands on a crowdsourced social bus to parcel destination, gets recharged by the wireless recharger deployed on the bus, and then flies from the bus to the parcel destination. This novel approach not only increases the delivery range and load capacity of battery-limited UAVs, but is also much more cost-effective and environment-friendly than traditional methods. New challenges therefore emerge as the buses with spatiotemporal mobility become the bottleneck during delivery. By landing on buses, an Energy-Neutral Flight Principle and a delivery scheduling algorithm are proposed for the UAVs. Using the Energy-Neutral Flight Principle, each UAV can plan a flying path without depleting energy given buses with uncertain velocities. Besides, the delivery scheduling algorithm optimizes the delivery time and number of delivered parcels given warehouse location, logistics UAVs, parcel locations and buses. Comprehensive evaluations using a large-scale bus dataset demonstrate the superiority of the innovative logistics UAV schedule approach.

scholarly journals Multi-Country Scale Assessment of Available Energy Recovery Potential Using Micro-Hydropower in Drinking, Pressurised Irrigation and Wastewater Networks, Covering Part of the EU

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 899
Author(s):  
Djordje Mitrovic ◽  
Miguel Crespo Chacón ◽  
Aida Mérida García ◽  
Jorge García Morillo ◽  
Juan Antonio Rodríguez Diaz ◽  
...  
Keyword(s):  
Drinking Water ◽  
Northern Ireland ◽  
Energy Recovery ◽  
Large Scale ◽  
Significant Proportion ◽  
Value Added ◽  
Cost Effective ◽  
Net Energy ◽  
Water Networks ◽  
Scale Assessment

Studies have shown micro-hydropower (MHP) opportunities for energy recovery and CO2 reductions in the water sector. This paper conducts a large-scale assessment of this potential using a dataset amassed across six EU countries (Ireland, Northern Ireland, Scotland, Wales, Spain, and Portugal) for the drinking water, irrigation, and wastewater sectors. Extrapolating the collected data, the total annual MHP potential was estimated between 482.3 and 821.6 GWh, depending on the assumptions, divided among Ireland (15.5–32.2 GWh), Scotland (17.8–139.7 GWh), Northern Ireland (5.9–8.2 GWh), Wales (10.2–8.1 GWh), Spain (375.3–539.9 GWh), and Portugal (57.6–93.5 GWh) and distributed across the drinking water (43–67%), irrigation (51–30%), and wastewater (6–3%) sectors. The findings demonstrated reductions in energy consumption in water networks between 1.7 and 13.0%. Forty-five percent of the energy estimated from the analysed sites was associated with just 3% of their number, having a power output capacity >15 kW. This demonstrated that a significant proportion of energy could be exploited at a small number of sites, with a valuable contribution to net energy efficiency gains and CO2 emission reductions. This also demonstrates cost-effective, value-added, multi-country benefits to policy makers, establishing the case to incentivise MHP in water networks to help achieve the desired CO2 emissions reductions targets.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

scholarly journals Anti-meningococcal B vaccination in Italian adolescents: a cost-effective health opportunity

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
D Panatto ◽  
P Landa ◽  
D Amicizia ◽  
P L Lai ◽  
E Lecini ◽  
...  
Keyword(s):  
Large Scale ◽  
Public Health Problem ◽  
Vaccination Strategy ◽  
Cost Effective ◽  
Dose Schedule ◽  
Developed Countries ◽  
Invasive Disease ◽  
International Setting ◽  
The Impact

Abstract Background Invasive disease due to Neisseria meningitidis (Nm) is a serious public health problem even in developed countries, owing to its high lethality rate (8-15%) and the invalidating sequelae suffered by many (up to 60%) survivors. As the microorganism is transmitted via the airborne route, the only available weapon in the fight against Nm invasive disease is vaccination. Our aim was to carry out an HTA to evaluate the costs and benefits of anti-meningococcal B (MenB) vaccination with Trumenba® in adolescents in Italy, while also considering the impact of this new vaccination strategy on organizational and ethics aspects. Methods A lifetime Markov model was developed. MenB vaccination with the two-dose schedule of Trumenba® in adolescents was compared with 'non-vaccination'. Two perspectives were considered: the National Health Service (NHS) and society. Three disease phases were defined: acute, post-acute and long-term. Epidemiological, economic and health utilities data were taken from Italian and international literature. The analysis was conducted by means of Microsoft Excel 2010®. Results Our study indicated that vaccinating adolescents (11th year of life) with Trumenba® was cost-effective with an ICER = € 7,912/QALY from the NHS perspective and € 7,758/QALY from the perspective of society. Vaccinating adolescents reduces the number of cases of disease due to meningococcus B in one of the periods of highest incidence of the disease, resulting in significant economic and health savings. Conclusions This is the first study to evaluate the overall impact of free MenB vaccination in adolescents both in Italy and in the international setting. Although cases of invasive disease due to meningococcus B are few, if the overall impact of the disease is adequately considered, it becomes clear that including anti-meningococcal B vaccination into the immunization program for adolescents is strongly recommended from the health and economic standpoints. Key messages Free, large-scale MenB vaccination is key to strengthening the global fight against invasive meningococcal disease. Anti-meningococcal B vaccination in adolescents is a cost-effective health opportunity.

scholarly journals Environmental Engineering Techniques to Restore Degraded Posidonia oceanica Meadows

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 661
Author(s):  
Luigi Piazzi ◽  
Stefano Acunto ◽  
Francesca Frau ◽  
Fabrizio Atzori ◽  
Maria Francesca Cinti ◽  
...  
Keyword(s):  
Large Scale ◽  
Natural Habitat ◽  
Cost Benefit ◽  
Cost Effective ◽  
Posidonia Oceanica ◽  
Environmental Engineering ◽  
Bottom Surface ◽  
Sessile Invertebrates ◽  
Set Up ◽  
Short Time

Seagrass planting techniques have shown to be an effective tool for restoring degraded meadows and ecosystem function. In the Mediterranean Sea, most restoration efforts have been addressed to the endemic seagrass Posidonia oceanica, but cost-benefit analyses have shown unpromising results. This study aimed at evaluating the effectiveness of environmental engineering techniques generally employed in terrestrial systems to restore the P. oceanica meadows: two different restoration efforts were considered, either exploring non-degradable mats or, for the first time, degradable mats. Both of them provided encouraging results, as the loss of transplanting plots was null or very low and the survival of cuttings stabilized to about 50%. Data collected are to be considered positive as the survived cuttings are enough to allow the future spread of the patches. The utilized techniques provided a cost-effective restoration tool likely affordable for large-scale projects, as the methods allowed to set up a wide bottom surface to restore in a relatively short time without any particular expensive device. Moreover, the mats, comparing with other anchoring methods, enhanced the colonization of other organisms such as macroalgae and sessile invertebrates, contributing to generate a natural habitat.

scholarly journals 2D-3D integration of hexagonal boron nitride and a high-κ dielectric for ultrafast graphene-based electro-absorption modulators

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hitesh Agarwal ◽  
Bernat Terrés ◽  
Lorenzo Orsini ◽  
Alberto Montanaro ◽  
Vito Sorianello ◽  
...  
Keyword(s):  
Boron Nitride ◽  
Hafnium Oxide ◽  
High Speed ◽  
Hexagonal Boron Nitride ◽  
Temperature Stability ◽  
Cost Effective ◽  
Fold Increase ◽  
Building Blocks ◽  
High Quality ◽  
New Device

AbstractElectro-absorption (EA) waveguide-coupled modulators are essential building blocks for on-chip optical communications. Compared to state-of-the-art silicon (Si) devices, graphene-based EA modulators promise smaller footprints, larger temperature stability, cost-effective integration and high speeds. However, combining high speed and large modulation efficiencies in a single graphene-based device has remained elusive so far. In this work, we overcome this fundamental trade-off by demonstrating the 2D-3D dielectric integration in a high-quality encapsulated graphene device. We integrated hafnium oxide (HfO2) and two-dimensional hexagonal boron nitride (hBN) within the insulating section of a double-layer (DL) graphene EA modulator. This combination of materials allows for a high-quality modulator device with high performances: a ~39 GHz bandwidth (BW) with a three-fold increase in modulation efficiency compared to previously reported high-speed modulators. This 2D-3D dielectric integration paves the way to a plethora of electronic and opto-electronic devices with enhanced performance and stability, while expanding the freedom for new device designs.

scholarly journals A Review on Terahertz Technologies Accelerated by Silicon Photonics

Nanomaterials ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1646
Author(s):  
Jingya Xie ◽  
Wangcheng Ye ◽  
Linjie Zhou ◽  
Xuguang Guo ◽  
Xiaofei Zang ◽  
...  
Keyword(s):  
Integrated Circuit ◽  
Silicon Photonics ◽  
Large Scale ◽  
Cost Effective ◽  
Single Chip ◽  
Photonic Integrated Circuit ◽  
Hybrid Silicon ◽  
The Cost ◽  
Silicon Based ◽  
Thz Applications

In the last couple of decades, terahertz (THz) technologies, which lie in the frequency gap between the infrared and microwaves, have been greatly enhanced and investigated due to possible opportunities in a plethora of THz applications, such as imaging, security, and wireless communications. Photonics has led the way to the generation, modulation, and detection of THz waves such as the photomixing technique. In tandem with these investigations, researchers have been exploring ways to use silicon photonics technologies for THz applications to leverage the cost-effective large-scale fabrication and integration opportunities that it would enable. Although silicon photonics has enabled the implementation of a large number of optical components for practical use, for THz integrated systems, we still face several challenges associated with high-quality hybrid silicon lasers, conversion efficiency, device integration, and fabrication. This paper provides an overview of recent progress in THz technologies based on silicon photonics or hybrid silicon photonics, including THz generation, detection, phase modulation, intensity modulation, and passive components. As silicon-based electronic and photonic circuits are further approaching THz frequencies, one single chip with electronics, photonics, and THz functions seems inevitable, resulting in the ultimate dream of a THz electronic–photonic integrated circuit.

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