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  1. Social computing is the study of how technology shapes human social interactions. This topic has become increasingly relevant to secondary school students (ages 11--18) as more of young people's everyday social experiences take place online, particularly with the continuing effects of the COVID-19 pandemic. However, social computing topics are rarely touched upon in existing middle and high school curricula. We seek to introduce concepts from social computing to secondary school students so they can understand how computing has wide-ranging social implications that touch upon their everyday lives, as well as think critically about both the positive and negative sides of different social technology designs. In this report, we present a series of six lessons combining presentations and hands-on activities covering topics within social computing and detail our experience teaching these lessons to approximately 1,405 students across 13 middle and high schools in our local school district. We developed lessons covering how social computing relates to the topics of Data Management, Encrypted Messaging, Human-Computer Interaction Careers, Machine Learning and Bias, Misinformation, and Online Behavior. We found that 81.13% of students expressed greater interest in the content of our lessons compared to their interest in STEM overall. We also found from pre- and post-lesson comprehension questions that 63.65% learned new concepts from the main activity. We release all lesson materials on a website for public use. From our experience, we observed that students were engaged in these topics and found enjoyment in finding connections between computing and their own lives. 
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    Free, publicly-accessible full text available March 7, 2025
  2. A central challenge in quantum networking is transferring quantum states between different physical modalities, such as between flying photonic qubits and stationary quantum memories. One implementation entails using spin–photon interfaces that combine solid-state spin qubits, such as color centers in diamond, with photonic nanostructures. However, while high-fidelity spin–photon interactions have been demonstrated on isolated devices, building practical quantum repeaters requires scaling to large numbers of interfaces yet to be realized. Here, we demonstrate integration of nanophotonic cavities containing tin-vacancy (SnV) centers in a photonic integrated circuit (PIC). Out of a six-channel quantum microchiplet (QMC), we find four coupled SnV-cavity devices with an average Purcell factor of ∼7. Based on system analyses and numerical simulations, we find with near-term improvements this multiplexed architecture can enable high-fidelity quantum state transfer, paving the way toward building large-scale quantum repeaters.

     
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  3. Olfactory navigation is observed across species and plays a crucial role in locating resources for survival. In the laboratory, understanding the behavioral strategies and neural circuits underlying odor-taxis requires a detailed understanding of the animal’s sensory environment. For small model organisms likeCaenorhabditis elegansand larvalDrosophila melanogaster, controlling and measuring the odor environment experienced by the animal can be challenging, especially for airborne odors, which are subject to subtle effects from airflow, temperature variation, and from the odor’s adhesion, adsorption, or reemission. Here, we present a method to control and measure airborne odor concentration in an arena compatible with an agar substrate. Our method allows continuous controlling and monitoring of the odor profile while imaging animal behavior. We construct stationary chemical landscapes in an odor flow chamber through spatially patterned odorized air. The odor concentration is measured with a spatially distributed array of digital gas sensors. Careful placement of the sensors allows the odor concentration across the arena to be continuously inferred in space and monitored through time. We use this approach to measure the odor concentration that each animal experiences as it undergoes chemotaxis behavior and report chemotaxis strategies forC. elegansandD. melanogasterlarvae populations as they navigate spatial odor landscapes.

     
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    Free, publicly-accessible full text available July 25, 2024
  4. Free, publicly-accessible full text available October 1, 2024
  5. Solid-state quantum emitters have emerged as a leading quantum memory for quantum networking applications. However, standard optical characterization techniques are neither efficient nor repeatable at scale. Here we introduce and demonstrate spectroscopic techniques that enable large-scale, automated characterization of colour centres. We first demonstrate the ability to track colour centres by registering them to a fabricated machine-readable global coordinate system, enabling a systematic comparison of the same colour centre sites over many experiments. We then implement resonant photoluminescence excitation in a widefield cryogenic microscope to parallelize resonant spectroscopy, achieving two orders of magnitude speed-up over confocal microscopy. Finally, we demonstrate automated chip-scale characterization of colour centres and devices at room temperature, imaging thousands of microscope fields of view. These tools will enable the accelerated identification of useful quantum emitters at chip scale, enabling advances in scaling up colour centre platforms for quantum information applications, materials science and device design and characterization. 
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    Free, publicly-accessible full text available November 1, 2024
  6. Current challenges for quantum repeaters using solid-state emitters include incorporating (1) multiple nearly-indistinguishable emitters (2) into an interposer with pho-tonic processing capabilities. We develop a process flow that targets both of these tasks.

     
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  7. A central goal in creating long-distance quantum networks and distributed quantum computing is the development of interconnected and individually controlled qubit nodes. Atom-like emitters in diamond have emerged as a leading system for optically networked quantum memories, motivating the development of visible-spectrum, multi-channel photonic integrated circuit (PIC) systems for scalable atom control. However, it has remained an open challenge to realize optical programmability with a qubit layer that can achieve high optical detection probability over many optical channels. Here, we address this problem by introducing a modular architecture of piezoelectrically actuated atom-control PICs (APICs) and artificial atoms embedded in diamond nanostructures designed for high-efficiency free-space collection. The high-speed four-channel APIC is based on a splitting tree mesh with triple-phase shifter Mach–Zehnder interferometers. This design simultaneously achieves optically broadband operation at visible wavelengths, high-fidelity switching (>40dB) at low voltages, submicrosecond modulation timescales (>30MHz), and minimal channel-to-channel crosstalk for repeatable optical pulse carving. Via a reconfigurable free-space interconnect, we use the APIC to address single silicon vacancy color centers in individual diamond waveguides with inverse tapered couplers, achieving efficient single photon detection probabilities (∼15%) and second-order autocorrelation measurementsg(2)(0)<0.14 for all channels. The modularity of this distributed APIC–quantum memory system simplifies the quantum control problem, potentially enabling further scaling to thousands of channels.

     
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  8. Louis, Matthieu (Ed.)
    Imaging neural activity in a behaving animal presents unique challenges in part because motion from an animal’s movement creates artifacts in fluorescence intensity time-series that are difficult to distinguish from neural signals of interest. One approach to mitigating these artifacts is to image two channels simultaneously: one that captures an activity-dependent fluorophore, such as GCaMP, and another that captures an activity-independent fluorophore such as RFP. Because the activity-independent channel contains the same motion artifacts as the activity-dependent channel, but no neural signals, the two together can be used to identify and remove the artifacts. However, existing approaches for this correction, such as taking the ratio of the two channels, do not account for channel-independent noise in the measured fluorescence. Here, we present Two-channel Motion Artifact Correction (TMAC), a method which seeks to remove artifacts by specifying a generative model of the two channel fluorescence that incorporates motion artifact, neural activity, and noise. We use Bayesian inference to infer latent neural activity under this model, thus reducing the motion artifact present in the measured fluorescence traces. We further present a novel method for evaluating ground-truth performance of motion correction algorithms by comparing the decodability of behavior from two types of neural recordings; a recording that had both an activity-dependent fluorophore and an activity-independent fluorophore (GCaMP and RFP) and a recording where both fluorophores were activity-independent (GFP and RFP). A successful motion correction method should decode behavior from the first type of recording, but not the second. We use this metric to systematically compare five models for removing motion artifacts from fluorescent time traces. We decode locomotion from a GCaMP expressing animal 20x more accurately on average than from control when using TMAC inferred activity and outperforms all other methods of motion correction tested, the best of which were ~8x more accurate than control. 
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  9. We demonstrate heterogeneous integration of solid-state nanophotonic cavities into a scalable photonic platform as an efficient optical interface for quantum memories based on diamond color centers.

     
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