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Publications
A graphical user interface for calculating the arterial input function during dynamic positron emission tomography
Purpose. Dynamic positron emission tomography (dPET) requires the acquisition of the arterial input function (AIF), conventionally obtained … (see more)via invasive arterial blood sampling. To obtain the AIF non-invasively, our group developed and combined two novel solutions consisting of (1) a detector, placed on a patient’s wrist during the PET scans to measure the radiation leaving the wrist and (2) a Geant4-based Monte Carlo simulation software. The simulations require patient-specific wrist geometry. The aim of this study was to develop a graphical user interface (GUI) allowing the user to import 2D ultrasound scans of a patient’s wrist, and measure the wrist features needed to calculate the AIF. Methods. The GUI elements were implemented using Qt5 and VTK-8.2.0. The user imports a patient’s wrist ultrasound scans, measures the radial artery and veins’ surface and depth to model a wrist phantom, then specifies the radioactive source used during the dPET scan. The phantom, the source, and the number of decay events are imported into the Geant4-based Monte Carlo software to run a simulation. In this study, 100 million decays of 18F and 68Ga were simulated in a wrist phantom designed based on an ultrasound scan. The detector’s efficiency was calculated and the results were analyzed using a clinical data processing algorithm developed in a previous study. Results. The detector’s total efficiency decreased by 3.5% for 18F and by 51.7% for 68Ga when using a phantom based on ultrasound scans compared to a generic wrist phantom. Similarly, the data processing algorithm’s accuracy decreased when using the patient-specific phantom, giving errors greater than 1.0% for both radioisotopes. Conclusions. This toolkit enables the user to run Geant4-based Monte Carlo simulations for dPET detector development applications using a patient-specific wrist phantom. Leading to a more precise simulation of the developed detector during dPET and the calculation of a personalized AIF.
Attention has become a common ingredient in deep learning architectures. It adds a dynamical selection of information on top of the static s… (see more)election of information supported by weights. In the same way, we can imagine a higher-order informational filter built on top of attention: an Attention Schema (AS), namely, a descriptive and predictive model of attention. In cognitive neuroscience, Attention Schema Theory (AST) supports this idea of distinguishing attention from AS. A strong prediction of this theory is that an agent can use its own AS to also infer the states of other agents' attention and consequently enhance coordination with other agents. As such, multi-agent reinforcement learning would be an ideal setting to experimentally test the validity of AST. We explore different ways in which attention and AS interact with each other. Our preliminary results indicate that agents that implement the AS as a recurrent internal control achieve the best performance. In general, these exploratory experiments suggest that equipping artificial agents with a model of attention can enhance their social intelligence.
Combinatorial optimization (CO) problems are often NP-hard and thus out of reach for exact algorithms, making them a tempting domain to appl… (see more)y machine learning methods. The highly structured constraints in these problems can hinder either optimization or sampling directly in the solution space. On the other hand, GFlowNets have recently emerged as a powerful machinery to efficiently sample from composite unnormalized densities sequentially and have the potential to amortize such solution-searching processes in CO, as well as generate diverse solution candidates. In this paper, we design Markov decision processes (MDPs) for different combinatorial problems and propose to train conditional GFlowNets to sample from the solution space. Efficient training techniques are also developed to benefit long-range credit assignment. Through extensive experiments on a variety of different CO tasks with synthetic and realistic data, we demonstrate that GFlowNet policies can efficiently find high-quality solutions. Our implementation is open-sourced at https://github.com/zdhNarsil/GFlowNet-CombOpt.
Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities. Further pro… (see more)gress in AI development could lead to capabilities that pose extreme risks, such as offensive cyber capabilities or strong manipulation skills. We explain why model evaluation is critical for addressing extreme risks. Developers must be able to identify dangerous capabilities (through"dangerous capability evaluations") and the propensity of models to apply their capabilities for harm (through"alignment evaluations"). These evaluations will become critical for keeping policymakers and other stakeholders informed, and for making responsible decisions about model training, deployment, and security.
Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities. Further pro… (see more)gress in AI development could lead to capabilities that pose extreme risks, such as offensive cyber capabilities or strong manipulation skills. We explain why model evaluation is critical for addressing extreme risks. Developers must be able to identify dangerous capabilities (through"dangerous capability evaluations") and the propensity of models to apply their capabilities for harm (through"alignment evaluations"). These evaluations will become critical for keeping policymakers and other stakeholders informed, and for making responsible decisions about model training, deployment, and security.
Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities. Further pro… (see more)gress in AI development could lead to capabilities that pose extreme risks, such as offensive cyber capabilities or strong manipulation skills. We explain why model evaluation is critical for addressing extreme risks. Developers must be able to identify dangerous capabilities (through"dangerous capability evaluations") and the propensity of models to apply their capabilities for harm (through"alignment evaluations"). These evaluations will become critical for keeping policymakers and other stakeholders informed, and for making responsible decisions about model training, deployment, and security.
Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities. Further pro… (see more)gress in AI development could lead to capabilities that pose extreme risks, such as offensive cyber capabilities or strong manipulation skills. We explain why model evaluation is critical for addressing extreme risks. Developers must be able to identify dangerous capabilities (through"dangerous capability evaluations") and the propensity of models to apply their capabilities for harm (through"alignment evaluations"). These evaluations will become critical for keeping policymakers and other stakeholders informed, and for making responsible decisions about model training, deployment, and security.
