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1. Design and fabrication of robust hybrid photonic crystal cavities

   https://doi.org/10.1515/nanoph-2024-0500  

   Abulnaga, Alex; Karg, Sean; Mukherjee, Sounak; Gupta, Adbhut; Baldwin, Kirk W; Pfeiffer, Loren N; de_Leon, Nathalie P (November 2024, Nanophotonics)

   Abstract Heterogeneously integrated hybrid photonic crystal cavities enable strong light–matter interactions with solid state, optically addressable quantum memories. A key challenge to realizing high quality factor (Q) hybrid photonic crystals is the reduced index contrast on the substrate compared to suspended devices in air. This challenge is particularly acute for color centers in diamond because of diamond’s high refractive index, which leads to increased scattering loss into the substrate. Here, we develop a design methodology for hybrid photonic crystals utilizing a detailed understanding of substrate-mediated loss, which incorporates sensitivity to fabrication errors as a critical parameter. Using this methodology, we design robust, high-Q, GaAs-on-diamond photonic crystal cavities, and by optimizing our fabrication procedure, we experimentally realize cavities withQapproaching 30,000 at a resonance wavelength of 955 nm. 

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2. Online Page Migration with ML Advice

   Indyk, P.; Mallmann-Trenn, F.; Mitrovic, S.; Rubinfeld, R. (January 2022, 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022))

   We consider online algorithms for the page migration problem that use predictions, potentially imperfect, to improve their performance. The best known online algorithms for this problem, due to Westbrook’94 and Bienkowski et al’17, have competitive ratios strictly bounded away from 1. In contrast, we show that if the algorithm is given a prediction of the input sequence, then it can achieve a competitive ratio that tends to 1 as the prediction error rate tends to 0. Specifically, the competitive ratio is equal to 1+O(q), where q is the prediction error rate. We also design a “fallback option” that ensures that the competitive ratio of the algorithm for any input sequence is at most O(1/q). Our result adds to the recent body of work that uses machine learning to improve the performance of “classic” algorithms. 

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3. Optimal Stopping Rules for Sequential Hypothesis Testing

   Daskalakis, Constantinos; Kawase, Yasushi (January 2017, 25th Annual European Symposium on Algorithms (ESA))

   Suppose that we are given sample access to an unknown distribution p over n elements and an explicit distribution q over the same n elements. We would like to reject the null hypothesis“p=q” after seeing as few samples as possible, whenp6=q, while we never want to reject the null, when p=q. Well-known results show thatΘ(√n/2)samples are necessary and sufficient for distinguishing whether p equals q versus p is-far from q in total variation distance. However,this requires the distinguishing radiusto be fixed prior to deciding how many samples to request.Our goal is instead to design sequential hypothesis testers, i.e. online algorithms that request i.i.d.samples from p and stop as soon as they can confidently reject the hypothesis p=q, without being given a lower bound on the distance between p and q, whenp6=q. In particular, we want to minimize the number of samples requested by our tests as a function of the distance between p and q, and if p=q we want the algorithm, with high probability, to never reject the null. Our work is motivated by and addresses the practical challenge of sequential A/B testing in Statistics.We show that, when n= 2, any sequential hypothesis test must seeΩ(1dtv(p,q)2log log1dtv(p,q))samples, with high (constant) probability, before it rejects p=q, where dtv(p,q) is the—unknown to the tester—total variation distance between p and q. We match the dependence of this lower bound ondtv(p,q)by proposing a sequential tester that rejects p=q from at most O(√ndtv(p,q)2log log1dtv(p,q))samples with high (constant) probability. TheΩ(√n)dependence on the support size n is also known to be necessary. We similarly provide two-sample sequential hypothesis testers, when sample access is given to both p and q, and discuss applications to sequential A/B testing. 

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4. Remotely Co-Designing Features for Communication Applications using Automatic Captioning with Deaf and Hearing Pairs

   https://doi.org/10.1145/3491102.3501843  

   Seita, Matthew; Lee, Sooyeon; Andrew, Sarah; Shinohara, Kristen; Huenerfauth, Matt (April 2022, Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI '22))

   Deaf and Hard-of-Hearing (DHH) users face accessibility challenges during in-person and remote meetings. While emerging use of applications incorporating automatic speech recognition (ASR) is promising, more user-interface and user-experience research is needed. While co-design methods could elucidate designs for such applications, COVID-19 has interrupted in-person research. This study describes a novel methodology for conducting online co-design workshops with 18 DHH and hearing participant pairs to investigate ASR-supported mobile and videoconferencing technologies along two design dimensions: Correcting errors in ASR output and implementing notification systems for influencing speaker behaviors. Our methodological findings include an analysis of communication modalities and strategies participants used, use of an online collaborative whiteboarding tool, and how participants reconciled differences in ideas. Finally, we present guidelines for researchers interested in online DHH co-design methodologies, enabling greater geographically diversity among study participants even beyond the current pandemic. 

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5. Quantitative Investigation into the Design and Process Parametric Effects on the Fiber‐Entrapped Residual Charge for a Polymer Melt Electrohydrodynamic Printing Process

   https://doi.org/10.1002/mame.202100766  

   Cao, Kai; Zhang, Fucheng; Zaeri, Ahmadreza; Zgeib, Ralf; Chang, Robert C. (January 2022, Macromolecular Materials and Engineering)

   Abstract The printing accuracy of the melt electrowriting (MEW) process is adversely affected by residual charge entrapped within the printed fibers. To mitigate this effect, the residual charge amount (Q_r) must first be accurately determined. In this study,Q_ris measured by a commercial electrometer at a nanocoulomb scale for MEW‐enabled scaffolds. Based on this enabling measurement, the effects of various design parameters (including substrate surface conductivityσ, printing timet, layer numberN), and process parameters (including voltageU, translational stage speedv, and material temperatureT_m), onQ_rare investigated. An increase ofσor decrease ofNhelps to decreaseQ_r. The effects of different process parameters on the residual charge can be either dependent or independent of fiber morphologies. Moreover, the fiber‐morphology dependent and independent effect can be either synergistic (UandT_m) or antagonistic (e.g.,v) for different process parameters. Under same conditions,Q_rin the interweaving scaffold design is generally smaller than that in the non‐interweaving scaffold design. These results help to furnish necessary insights into the charge dissipation process for a melt‐based electrohydrodynamic printing process while providing a systematic methodology to mitigate the residual charge accumulation. 

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