270,958 research outputs found
Artificial intelligence and UK national security: Policy considerations
RUSI was commissioned by GCHQ to conduct an independent research study into the use of artificial intelligence (AI) for national security purposes. The aim of this project is to establish an independent evidence base to inform future policy development regarding national security uses of AI. The findings are based on in-depth consultation with stakeholders from across the UK national security community, law enforcement agencies, private sector companies, academic and legal experts, and civil society representatives. This was complemented by a targeted review of existing literature on the topic of AI and national security.
The research has found that AI offers numerous opportunities for the UK national security community to improve efficiency and effectiveness of existing processes. AI methods can rapidly derive insights from large, disparate datasets and identify connections that would otherwise go unnoticed by human operators. However, in the context of national security and the powers given to UK intelligence agencies, use of AI could give rise to additional privacy and human rights considerations which would need to be assessed within the existing legal and regulatory framework. For this reason, enhanced policy and guidance is needed to ensure the privacy and human rights implications of national security uses of AI are reviewed on an ongoing basis as new analysis methods are applied to data
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Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network.
PurposeTo assess feasibility of training a convolutional neural network (CNN) to automate liver segmentation across different imaging modalities and techniques used in clinical practice and apply this to enable automation of liver biometry.MethodsWe trained a 2D U-Net CNN for liver segmentation in two stages using 330 abdominal MRI and CT exams acquired at our institution. First, we trained the neural network with non-contrast multi-echo spoiled-gradient-echo (SGPR)images with 300 MRI exams to provide multiple signal-weightings. Then, we used transfer learning to generalize the CNN with additional images from 30 contrast-enhanced MRI and CT exams.We assessed the performance of the CNN using a distinct multi-institutional data set curated from multiple sources (n = 498 subjects). Segmentation accuracy was evaluated by computing Dice scores. Utilizing these segmentations, we computed liver volume from CT and T1-weighted (T1w) MRI exams, and estimated hepatic proton- density-fat-fraction (PDFF) from multi-echo T2*w MRI exams. We compared quantitative volumetry and PDFF estimates between automated and manual segmentation using Pearson correlation and Bland-Altman statistics.ResultsDice scores were 0.94 ± 0.06 for CT (n = 230), 0.95 ± 0.03 (n = 100) for T1w MR, and 0.92 ± 0.05 for T2*w MR (n = 169). Liver volume measured by manual and automated segmentation agreed closely for CT (95% limit-of-agreement (LoA) = [-298 mL, 180 mL]) and T1w MR (LoA = [-358 mL, 180 mL]). Hepatic PDFF measured by the two segmentations also agreed closely (LoA = [-0.62%, 0.80%]).ConclusionsUtilizing a transfer-learning strategy, we have demonstrated the feasibility of a CNN to be generalized to perform liver segmentations across different imaging techniques and modalities. With further refinement and validation, CNNs may have broad applicability for multimodal liver volumetry and hepatic tissue characterization
Allocation in Practice
How do we allocate scarcere sources? How do we fairly allocate costs? These
are two pressing challenges facing society today. I discuss two recent projects
at NICTA concerning resource and cost allocation. In the first, we have been
working with FoodBank Local, a social startup working in collaboration with
food bank charities around the world to optimise the logistics of collecting
and distributing donated food. Before we can distribute this food, we must
decide how to allocate it to different charities and food kitchens. This gives
rise to a fair division problem with several new dimensions, rarely considered
in the literature. In the second, we have been looking at cost allocation
within the distribution network of a large multinational company. This also has
several new dimensions rarely considered in the literature.Comment: To appear in Proc. of 37th edition of the German Conference on
Artificial Intelligence (KI 2014), Springer LNC
Novel Artificial Human Optimization Field Algorithms - The Beginning
New Artificial Human Optimization (AHO) Field Algorithms can be created from
scratch or by adding the concept of Artificial Humans into other existing
Optimization Algorithms. Particle Swarm Optimization (PSO) has been very
popular for solving complex optimization problems due to its simplicity. In
this work, new Artificial Human Optimization Field Algorithms are created by
modifying existing PSO algorithms with AHO Field Concepts. These Hybrid PSO
Algorithms comes under PSO Field as well as AHO Field. There are Hybrid PSO
research articles based on Human Behavior, Human Cognition and Human Thinking
etc. But there are no Hybrid PSO articles which based on concepts like Human
Disease, Human Kindness and Human Relaxation. This paper proposes new AHO Field
algorithms based on these research gaps. Some existing Hybrid PSO algorithms
are given a new name in this work so that it will be easy for future AHO
researchers to find these novel Artificial Human Optimization Field Algorithms.
A total of 6 Artificial Human Optimization Field algorithms titled "Human
Safety Particle Swarm Optimization (HuSaPSO)", "Human Kindness Particle Swarm
Optimization (HKPSO)", "Human Relaxation Particle Swarm Optimization (HRPSO)",
"Multiple Strategy Human Particle Swarm Optimization (MSHPSO)", "Human Thinking
Particle Swarm Optimization (HTPSO)" and "Human Disease Particle Swarm
Optimization (HDPSO)" are tested by applying these novel algorithms on Ackley,
Beale, Bohachevsky, Booth and Three-Hump Camel Benchmark Functions. Results
obtained are compared with PSO algorithm.Comment: 25 pages, 41 figure
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