From a “Lazy Boy” to the Open Sea. The journey of making “Call Me Cappy”

In this paper I will describe and analyze the process of creating my thesis film, Call Me Cappy. I will start with stating the theme and discuss each aspect of this process in relation to the theme that originated this project. I will detail my biggest challenges and struggles. I will also try to show how the knowledge I absorbed through attending the graduate film program, and the literature I have read, has served my journey as a filmmaker. In the end, I will attempt to evaluate whether my theme found its full expression through this film. The final analysis will determine how well I was able to incorporate all the aspects of storytelling into creating a coherent piece of work

Oxygenated compounds in aged biomass burning plumes over the Eastern Mediterranean: evidence for strong secondary production of methanol and acetone

International audienceAirborne measurements of acetone, methanol, PAN, acetonitrile (by Proton Transfer Reaction Mass Spectrometry), and CO (by Tunable Diode Laser Absorption Spectroscopy) have been performed during the Mediterranean Intensive Oxidants Study (MINOS August 2001). We have identified ten biomass burning plumes from strongly elevated acetonitrile mixing ratios. The characteristic biomass burning signatures obtained from these plumes reveal secondary production of acetone and methanol, while CO photochemically declines in the plumes. Mean excess mixing ratios - normalized to CO - of 1.8%, 0.20%, 3.8%, and 0.65% for acetone, acetonitrile, methanol, and PAN, respectively, were found. By scaling to an assumed global annual source of 663-807Tg CO, biomass burning emissions of 25-31 and 29-35 Tg/yr for acetone and methanol are estimated, respectively. Our measurements suggest that the present biomass burning contributions of acetone and methanol are significantly underestimated due to the neglect of secondary formation within the plume. Median acetonitrile mixing ratios throughout the troposphere were around 150pmol/mol, in accord with current biomass burning inventories and an atmospheric lifetime of ~6 months

Explainable AI: The new 42?

Explainable AI is not a new field. Since at least the early exploitation of C.S. Pierce’s abductive reasoning in expert systems of the 1980s, there were reasoning architectures to support an explanation function for complex AI systems, including applications in medical diagnosis, complex multi-component design, and reasoning about the real world. So explainability is at least as old as early AI, and a natural consequence of the design of AI systems. While early expert systems consisted of handcrafted knowledge bases that enabled reasoning over narrowly well-defined domains (e.g., INTERNIST, MYCIN), such systems had no learning capabilities and had only primitive uncertainty handling. But the evolution of formal reasoning architectures to incorporate principled probabilistic reasoning helped address the capture and use of uncertain knowledge. There has been recent and relatively rapid success of AI/machine learning solutions arises from neural network architectures. A new generation of neural methods now scale to exploit the practical applicability of statistical and algebraic learning approaches in arbitrarily high dimensional spaces. But despite their huge successes, largely in problems which can be cast as classification problems, their effectiveness is still limited by their un-debuggability, and their inability to “explain” their decisions in a human understandable and reconstructable way. So while AlphaGo or DeepStack can crush the best humans at Go or Poker, neither program has any internal model of its task; its representations defy interpretation by humans, there is no mechanism to explain their actions and behaviour, and furthermore, there is no obvious instructional value.. the high performance systems can not help humans improve. Even when we understand the underlying mathematical scaffolding of current machine learning architectures, it is often impossible to get insight into the internal working of the models; we need explicit modeling and reasoning tools to explain how and why a result was achieved. We also know that a significant challenge for future AI is contextual adaptation, i.e., systems that incrementally help to construct explanatory models for solving real-world problems. Here it would be beneficial not to exclude human expertise, but to augment human intelligence with artificial intelligence

Cooperative subwavelength molecular quantum emitter arrays

Dipole-coupled subwavelength quantum emitter arrays respond cooperatively to external light fields as they may host collective delocalized excitations (a form of excitons) with super- or subradiant character. Deeply subwavelength separations typically occur in molecular ensembles, where in addition to photon-electron interactions, electron-vibron couplings and vibrational relaxation processes play an important role. We provide analytical and numerical results on the modification of super- and subradiance in molecular rings of dipoles including excitations of the vibrational degrees of freedom. While vibrations are typically considered detrimental to coherent dynamics, we show that molecular dimers or rings can be operated as platforms for the preparation of long-lived dark superposition states aided by vibrational relaxation. In closed ring configurations, we extend previous predictions for the generation of coherent light from ideal quantum emitters to molecular emitters, quantifying the role of vibronic coupling onto the output intensity and coherence