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   https://doi.org/10.1364/OE.535014  

   Trask, Lawrence_Robert; Pericherla, Srinivas_Varma; Delfyett, Peter_J (January 2025, Optics Express)

   We demonstrate a widely spaced, stabilized, and self-referenced opto-electronic oscillator driven electro-optic modulator based optical frequency comb. Using an ultra-stable Fabry-Perot etalon as a stable reference, we simultaneously stabilize a CW laser and generate a low noise and stable RF oscillation used to drive an electro-optic comb. In such a manner, the Fabry-Perot etalon pins both the carrier-envelope-offset frequency (f_ceo) and the repetition rate of the comb in place (f_rep), eliminating the need for an external RF oscillator. Usage of the ultra-stable Fabry-Perot etalon as both an optical and RF reference allows the removal of an external RF oscillator. Additionally, we determined the key parameters in producing high contrast ultrashort pulses necessary for coherent octave spanning supercontinuum generation using long and weak pulses associated with electro-optic modulator based combs. By using a monolithically fiber based pulse compression scheme, we produced ultrashort pulses to facilitate measuring the carrier-envelope-offset frequency, allowing for the first self-starting, self-stabilized, and self-referenced opto-electronic oscillator driven electro-optic modulator based optical frequency comb. 

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2. Self-Supervised Camera Self-Calibration from Video

   https://doi.org/10.1109/ICRA46639.2022.9811784  

   Fang, Jiading; Vasiljevic, Igor; Guizilini, Vitor; Ambrus, Rares; Shakhnarovich, Greg; Gaidon, Adrien; Walter, Matthew R (May 2022, IEEE)

   Camera calibration is integral to robotics and computer vision algorithms that seek to infer geometric properties of the scene from visual input streams. In practice, calibration is a laborious procedure requiring specialized data collection and careful tuning. This process must be repeated whenever the parameters of the camera change, which can be a frequent occurrence for mobile robots and autonomous vehicles. In contrast, self-supervised depth and ego-motion estimation approaches can bypass explicit calibration by in-ferring per-frame projection models that optimize a view-synthesis objective. In this paper, we extend this approach to explicitly calibrate a wide range of cameras from raw videos in the wild. We propose a learning algorithm to regress per-sequence calibration parameters using an efficient family of general camera models. Our procedure achieves self-calibration results with sub-pixel reprojection error, outperforming other learning-based methods. We validate our approach on a wide variety of camera geometries, including perspective, fisheye, and catadioptric. Finally, we show that our approach leads to improvements in the downstream task of depth estimation, achieving state-of-the-art results on the EuRoC dataset with greater computational efficiency than contemporary methods. 

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   https://doi.org/10.1039/c7dt03399k  

   Holloway, Lauren R.; Bogie, Paul M.; Hooley, Richard J. (January 2017, Dalton Transactions)

   In this frontier article we highlight recent advances in subcomponent self-sorting in self-assembled metal–ligand cage complexes, with a focus on selective discrimination between ligands that contain highly similar metal-coordinating groups. Effects such as varying ligand length, coordination angle and backbone flexibility, as well as the introduction of secondary weak forces such as hydrogen bonds can be exploited to favor either narcissistic or social self-sorting. We highlight these creative solutions, and emphasize the challenges that remain in the development of functional self-assembled heterocomplexes. 

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5. Self-E: Smartphone-Supported Guidance for Customizable Self-Experimentation

   https://doi.org/10.1145/3411764.3445100  

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   The ubiquity of self-tracking devices and smartphone apps has empowered people to collect data about themselves and try to self-improve. However, people with little to no personal analytics experience may not be able to analyze data or run experiments on their own (self-experiments). To lower the barrier to intervention-based self-experimentation, we developed an app called Self-E, which guides users through the experiment. We conducted a 2-week diary study with 16 participants from the local population and a second study with a more advanced group of users to investigate how they perceive and carry out self-experiments with the help of Self-E, and what challenges they face. We find that users are influenced by their preconceived notions of how healthy a given behavior is, making it difficult to follow Self-E’s directions and trusting its results. We present suggestions to overcome this challenge, such as by incorporating empathy and scaffolding in the system. 

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