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1. Inverse-designed broadband low-loss grating coupler on thick lithium-niobate-on-insulator platform

   https://doi.org/10.1063/5.0184413  

   Xie, Yijun; Nie, Mingming; Huang, Shu-Wei (January 2024, Applied Physics Letters)

   A grating coupler on 700-nm-thick Z-cut lithium-niobate-on-insulator platform with high coupling efficiency, large bandwidth, and high fabrication tolerance is designed and optimized by inverse design method. The optimized grating coupler is fabricated with a single set of e-beam lithography and etching process, and it is experimentally characterized to possess peak coupling efficiency of −3.8 dB at 1574.93 nm, 1 dB bandwidth of 71.7 nm, and 3 dB bandwidth of over 120 nm, respectively. 

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2. Optimal bandwidth choice for robust bias-corrected inference in regression discontinuity designs

   https://doi.org/10.1093/ectj/utz022  

   Calonico, Sebastian; Cattaneo, Matias D; Farrell, Max H (November 2019, The Econometrics Journal)

   Summary Modern empirical work in regression discontinuity (RD) designs often employs local polynomial estimation and inference with a mean square error (MSE) optimal bandwidth choice. This bandwidth yields an MSE-optimal RD treatment effect estimator, but is by construction invalid for inference. Robust bias-corrected (RBC) inference methods are valid when using the MSE-optimal bandwidth, but we show that they yield suboptimal confidence intervals in terms of coverage error. We establish valid coverage error expansions for RBC confidence interval estimators and use these results to propose new inference-optimal bandwidth choices for forming these intervals. We find that the standard MSE-optimal bandwidth for the RD point estimator is too large when the goal is to construct RBC confidence intervals with the smaller coverage error rate. We further optimize the constant terms behind the coverage error to derive new optimal choices for the auxiliary bandwidth required for RBC inference. Our expansions also establish that RBC inference yields higher-order refinements (relative to traditional undersmoothing) in the context of RD designs. Our main results cover sharp and sharp kink RD designs under conditional heteroskedasticity, and we discuss extensions to fuzzy and other RD designs, clustered sampling, and pre-intervention covariates adjustments. The theoretical findings are illustrated with a Monte Carlo experiment and an empirical application, and the main methodological results are available in R and Stata packages. 

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3. Physical-layer impairment estimation for random bandwidth traffic

   https://doi.org/10.1364/JOCN.506424  

   Xu, Yuxin; Agrell, Erik; Brandt-Pearce, Maite (January 2024, Journal of Optical Communications and Networking)

   Traffic demands in future elastic optical networks are expected to be heterogeneous with time-varying bandwidth. Estimating the physical-layer impairments (PLIs) for random bandwidth demands is important for cross-layer network resource provisioning. State-of-the-art PLI estimation techniques yield conservative PLI estimates using the maximum bandwidth, which leads to significant over-provisioning. This paper uses probabilistic information on random bandwidth demands to provide a computationally efficient, accurate, and flexible PLI estimate. The proposed model is consistent with the needs of future self-configuring fiber-optic networks and maximally avoids up to a 25% overestimation of PLIs compared to the benchmark for the cases studied, thus reducing the network design margin at a negligible extra computational cost. 

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4. Wi-Flex: Reflex Detection with Commodity WiFi

   https://doi.org/10.1145/3610930  

   Torun, Mert; Mostofi, Yasamin (September 2023, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   In this paper, we are interested in startle reflex detection with WiFi signals. We propose that two parameters related to the received signal bandwidth, maximum normalized bandwidth and bandwidth-intense duration, can successfully detect reflexes and robustly differentiate them from non-reflex events, even from those that involve intense body motions (e.g., certain exercises). In order to confirm this, we need a massive RF reflex dataset which would be prohibitively laborious to collect. On the other hand, there are many available reflex/non-reflex videos online. We then propose an efficient way of translating the content of a video to the bandwidth of the corresponding received RF signal that would have been measured if there was a link near the event in the video, by drawing analogies between our problem and the classic bandwidth modeling work of J. Carson in the context of analog FM radios (Carson's Rule). This then allows us to translate online reflex/non-reflex videos to an instant large RF bandwidth dataset, and characterize optimum 2D reflex/non-reflex decision regions accordingly, to be used during real operation with WiFi. We extensively test our approach with 203 reflex events, 322 non-reflex events (including 142 intense body motion events), over four areas (including several through-wall ones), and with 15 participants, achieving a correct reflex detection rate of 90.15% and a false alarm rate of 2.49% (all events are natural). While the paper is extensively tested with startle reflexes, it is also applicable to sport-type reflexes, and is thus tested with sport-related reflexes as well. We further show reflex detection with multiple people simultaneously engaged in a series of activities. Optimality of the proposed design is also demonstrated experimentally. Finally, we conduct experiments to show the potential of our approach for providing cost-effective and quantifiable metrics in sports, by quantifying a goalkeeper's reaction. Overall, our results confirm a fast, robust, and cost-effective reflex detection system, without collecting any RF training data, or training a neural network. 

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5. Coordinated CTA Combination and Bandwidth Partitioning for GPU Concurrent Kernel Execution

   https://doi.org/10.1145/3326124  

   Lin, Zhen; Dai, Hongwen; Mantor, Michael; Zhou, Huiyang (August 2019, ACM Transactions on Architecture and Code Optimization)

   null (Ed.)

   Contemporary GPUs support multiple kernels to run concurrently on the same streaming multiprocessors (SMs). Recent studies have demonstrated that such concurrent kernel execution (CKE) improves both resource utilization and computational throughput. Most of the prior works focus on partitioning the GPU resources at the cooperative thread array (CTA) level or the warp scheduler level to improve CKE. However, significant performance slowdown and unfairness are observed when latency-sensitive kernels co-run with bandwidth-intensive ones. The reason is that bandwidth over-subscription from bandwidth-intensive kernels leads to much aggravated memory access latency, which is highly detrimental to latency-sensitive kernels. Even among bandwidth-intensive kernels, more intensive kernels may unfairly consume much higher bandwidth than less-intensive ones. In this article, we first make a case that such problems cannot be sufficiently solved by managing CTA combinations alone and reveal the fundamental reasons. Then, we propose a coordinated approach for CTA combination and bandwidth partitioning. Our approach dynamically detects co-running kernels as latency sensitive or bandwidth intensive. As both the DRAM bandwidth and L2-to-L1 Network-on-Chip (NoC) bandwidth can be the critical resource, our approach partitions both bandwidth resources coordinately along with selecting proper CTA combinations. The key objective is to allocate more CTA resources for latency-sensitive kernels and more NoC/DRAM bandwidth resources to NoC-/DRAM-intensive kernels. We achieve it using a variation of dominant resource fairness (DRF). Compared with two state-of-the-art CKE optimization schemes, SMK [52] and WS [55], our approach improves the average harmonic speedup by 78% and 39%, respectively. Even compared to the best possible CTA combinations, which are obtained from an exhaustive search among all possible CTA combinations, our approach improves the harmonic speedup by up to 51% and 11% on average. 

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