Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization

We tackle the problem of sampling from intractable high-dimensional density functions, a fundamental task that often appears in machine learning and statistics. We extend recent sampling-based approaches that leverage controlled stochastic processes to model approximate samples from these target densities. The main drawback of these approaches is that the training objective requires full trajectories to compute, resulting in sluggish credit assignment issues due to use of entire trajectories and a learning signal present only at the terminal time. In this work, we present Diffusion Generative Flow Samplers (DGFS), a sampling-based framework where the learning process can be tractably broken down into short partial trajectory segments, via parameterizing an additional "flow function". Our method takes inspiration from the theory developed for generative flow networks (GFlowNets), allowing us to make use of intermediate learning signals. Through various challenging experiments, we demonstrate that DGFS achieves more accurate estimates of the normalization constant than closely-related prior methods.Comment: Accepted by ICLR 202

Intentional polarity conversion of AlN epitaxial layers by oxygen

Nitride materials (AlN, GaN, InN and their alloys) are commonly used in optoelectronics, high-power and high-frequency electronics. Polarity is the essential characteristic of these materials: when grown along c-direction, the films may exhibit either N- or metal-polar surface, which strongly influences their physical properties. The possibility to manipulate the polarity during growth allows to establish unique polarity in nitride thin films and nanowires for existing applications but also opens up new opportunities for device applications, e.g., in non-linear optics. In this work, we show that the polarity of an AlN film can intentionally be inverted by applying an oxygen plasma. We anneal an initially mixed-polar AlN film, grown on sapphire substrate by metal-organic vapor phase epitaxy (MOVPE), with an oxygen plasma in a molecular beam epitaxy (MBE) chamber; then, back in MOVPE, we deposit a 200 nm thick AlN film on top of the oxygen-treated surface. Analysis by high-resolution probe-corrected scanning transmission electron microscopy (STEM) imaging and electron energy-loss spectroscopy (EELS) evidences a switch of the N-polar domains to metal polarity. The polarity inversion is mediated through the formation of a thin AlxOyNz layer on the surface of the initial mixed polar film, induced by the oxygen annealing

CdZnTe strip detectors as sub-millimeter resolution imaging gamma radiation spectrometers

We report γ-ray detection performance measurements and computer simulations of a sub-millimeter pitch CdZnTe strip detector. The detector is a prototype for γ-ray measurements in the range of 20-600 keV. The prototype is a 1.5 mm thick, 64×64 orthogonal stripe CdZnTe detector of 0.375 mm pitch in both dimensions, with approximately one square inch of sensitive area. Using discrete laboratory electronics to process signals from an 8×8 stripe region of the prototype we measured good spectroscopic uniformity and sub-pitch (~0.2 mm) spatial resolution in both x and y dimensions. We present below measurements of the spatial uniformity, relative timing and pulse height of the anode and cathode signals. We simulated the photon interactions and signal generation in the strip detector and the test electronics and we compare these results with the data. The data indicate that cathode signal-as well as the anode signal-arises more strongly from the conduction electrons rather than the holes

Performance of CdZnTe strip detectors as sub-millimeter resolution imaging gamma radiation spectrometers

We report & gamma;-ray detection performance measurements and computer simulations of a sub-millimeter pitch CdZnTe strip detector. The detector is a prototype for & gamma;-ray astronomy measurements in the range of 20-200 keV. The prototype is a 1.5 mm thick, 64×64 orthogonal stripeCdZnTe detector of 0.375 mm pitch in both dimensions, with approximately one square inch of sensitive area. Using discrete laboratory electronics to process signals from an 8×8 stripe region of the prototype we measured good spectroscopic uniformity and sub-pitch (~0.2 mm) spatial resolution in both x and y dimensions. We present below measurements of the spatial uniformity, relative timing and pulse height of the anode and cathode signals, and the photon detection efficiency. We also present a technique for determining the location of the event in the third dimension (depth). We simulated the photon interactions and signal generation in the strip detector and the test electronics and we compare these results with the data. The data indicate that the cathode signal-as well as the anode signal-arises more strongly from the conduction electrons rather than the holes

Looking back on 50 years of literature to understand the potential impact of influenza on extrapulmonary medical outcomes

We conducted a scoping review of the epidemiological literature from the past 50 years to document the contribution of influenza virus infection to extrapulmonary clinical outcomes. We identified 99 publications reporting 243 associations using many study designs, exposure and outcome definitions, and methods. Laboratory confirmation of influenza was used in only 28 (12%) estimates, mostly in case-control and self-controlled case series study designs. We identified 50 individual clinical conditions associated with influenza. The most numerous estimates were of cardiocirculatory diseases, neurological/neuromuscular diseases, and fetal/newborn disorders, with myocardial infarction the most common individual outcome. Due to heterogeneity, we could not generate summary estimates of effect size, but of 130 relative effect estimates, 105 (81%) indicated an elevated risk of extrapulmonary outcome with influenza exposure. The literature is indicative of systemic complications of influenza virus infection, the requirement for more effective influenza control, and a need for robust confirmatory studies

Collective Animal Behavior from Bayesian Estimation and Probability Matching

Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is based on empirical fits to observations and we lack first-principles approaches that allow their derivation. Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty. We build a decision-making model with two stages: Bayesian estimation and probabilistic matching.
In the first stage, each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals. In the probability matching stage, each animal chooses a behavior with a probability given by the Bayesian estimation that this behavior is the most appropriate one. This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters, one that each animal assigns to the other animals and another given by the quality of the non-social information. We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling fish species. The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging, mate selection, neurobiology and psychology, and gives predictions for experiments directly testing the relationship between estimation and collective behavior