Qutrit Randomized Benchmarking by Morvan, A, Ramasesh, VV, Blok, MS, Kreikebaum, JM, O’Brien, K, Chen, L, Mitchell, BK, Naik, RK, Santiago, DI, Siddiqi, I

“…Finally, we apply cycle benchmarking to a two-qutrit CSUM gate and obtain a two-qutrit process fidelity of 0.85. …”
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IDEBench: A Benchmark for Interactive Data Exploration by Eichmann, Philipp, Zgraggen, Emanuel, Binnig, Carsten, Kraska, Tim

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A Benchmark for Systematic Generalization in Grounded Language Understanding by Ruis, Laura, Andreas, Jacob, Baroni, Marco, Bouchacourt, Diane, Lake, Brenden M

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BESDUI: A Benchmark for End-User Structured Data User Interfaces by García, Roberto, Gil, Rosa, Gimeno, Juan Manuel, Karger, David R, Bakke, Eirik

“…In this article, we propose and demonstrate the use of a benchmark for evaluating such user interfaces, similar to the TREC benchmark for evaluating traditional search engines. …”
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Hierarchical neural networks for sequential sentence classification in medical scientific abstracts by Jin, Di, Szolovits, Peter

“…Our model outperforms the state-of-the-art results by 2%-3% on two benchmarking datasets for sequential sentence classification in medical scientific abstracts.…”
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Efficient Processing of Deep Neural Networks: A Tutorial and Survey by Sze, Vivienne, Chen, Yu-Hsin, Yang, Tien-Ju, Emer, Joel S

“…It will also summarize various development resources that enable researchers and practitioners to quickly get started in this field, and highlight important benchmarking metrics and design considerations that should be used for evaluating the rapidly growing number of DNN hardware designs, optionally including algorithmic codesigns, being proposed in academia and industry. …”
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Energy efficient random access for the quasi-static fading MAC by Kowshik, Suhas S.(Suhas Subramanya), Andreev, Kirill, Frolov, Alexey, Polyanskiy, Yury

“…Finally, we mention that while a number of candidate solutions (MUSA, SCMA, RSMA, etc.) are being discussed for the 5G, the information-theoretic analysis and benchmarking has not been attempted before (in part due to lack of common random-access model). …”
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Experimental assessment of human-robot teaming for multi-step remote manipulation with expert operators by P?rez-D'Arpino, Claudia, Khurshid, Rebecca, Shah, Julie

“…Our study protocol, metrics and models for statistical analysis might also serve as a general benchmarking framework in teleoperation domains.…”
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A Simple Semiempirical Short-Channel MOSFET Current-Voltage Model Continuous Across All Regions of Operation and Employing Only Physical Parameters by Khakifirooz, A., Nayfeh, Osama M., Antoniadis, Dimitri A.

“…The simplicity of the model and the fact that it only uses physically meaningful parameters provides an easy way for technology benchmarking and performance projection.…”
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Game Theory based Peer Grading Mechanisms for MOOCs by Wu, William, Kaashoek, Nicolaas, Tzamos, Christos, Weinberg, Matthew, Daskalakis, Konstantinos

“…The mathematical model and benchmark opens the way for future derivative works to be performed and compared.…”
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ImageNet Large Scale Visual Recognition Challenge by Russakovsky, Olga, Deng, Jia, Su, Hao, Krause, Jonathan, Satheesh, Sanjeev, Ma, Sean, Huang, Zhiheng, Karpathy, Andrej, Khosla, Aditya, Bernstein, Michael, Berg, Alexander C., Fei-Fei, Li

“…The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. …”
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Scene parsing through ADE20K dataset by Zhou, Bolei, Zhao, Hang, Puig Fernandez, Francesco Xavier, Fidler, Sanja, Barriuso, Adela, Torralba, Antonio

“…Several segmentation baseline models are evaluated on the benchmark. A novel network design called Cascade Segmentation Module is proposed to parse a scene into stuff, objects, and object parts in a cascade and improve over the baselines. …”
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HACS: Human Action Clips and Segments Dataset for Recognition and Temporal Localization by Zhao, Hang, Torralba, Antonio, Torresani, Lorenzo, Yan, Zhicheng

“…HACS Clips contains more labeled examples than any existing video benchmark. This renders our dataset both a large-scale action recognition benchmark and an excellent source for spatiotemporal feature learning. …”
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On the Computation of Power in Volume Integral Equation Formulations by Polimeridis, Athanasios G., Reid, M. T. Homer, Johnson, Steven G., White, Jacob K., Rodriguez, Alejandro W.

“…The formulas are validated against results obtained both with analytical and numerical methods for scattering and radiation benchmark cases.…”
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PrORAM: dynamic prefetcher for oblivious RAM by Yu, Xiangyao, Ren, Ling, Fletcher, Christopher Wardlaw, Kwon, Albert Hyukjae, Devadas, Srinivas

“…PrORAMachieves an average performance gain of 20% over the base-line ORAM for memory intensive benchmarks among Splash2and 5.5% for SPEC06 workloads. …”
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Classification using geometric level sets by Varshney, Kush R., Willsky, Alan S.

“…Geometric level set classification yields performance results on benchmark data sets that are competitive with well-established methods.…”
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Nanostructured GaN transistors by Chowdhury, Nadim, Palacios, Tomas

“…The theoretical performance of these ultra-scaled devices is benchmarked with respect to other competing technologies.…”
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Inductive program synthesis over noisy data by Handa, S, Rinard, MC

“…Results from our implemented system running on problems from the SyGuS 2018 benchmark suite highlight its ability to successfully synthesize programs in the face of noisy data sets, including the ability to synthesize a correct program even when every input-output example in the data set is corrupted.…”
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Photophysics of GaN single-photon emitters in the visible spectral range by Berhane, Amanuel M., Bradac, Carlo, Toth, Milos, Aharonovich, Igor, Jeong, Kwang-Yong, Walsh, Michael E, Englund, Dirk R.

“…Our results underpin some of the fundamental properties of SPEs in GaN both at cryogenic and RT, and define the benchmark for future work in GaN-based single-photon technologies.…”
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Skip-thought Vectors by Kiros, Ryan, Zhu, Yukun, Salakhutdinov, Ruslan R., Zemel, Richard, Urtasun, Raquel, Fidler, Sanja, Torralba, Antonio

“…After training our model, we extract and evaluate our vectors with linear models on 8 tasks: semantic relatedness, paraphrase detection, image-sentence ranking, question-type classification and 4 benchmark sentiment and subjectivity datasets. The end result is an off-the-shelf encoder that can produce highly generic sentence representations that are robust and perform well in practice. …”
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