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- Pennsylvania State UniversityUS13 papers
- Los Alamos National LaboratoryUS10 papers
- National Institute of Standards and TechnologyUS9 papers
- LSU New OrleansUS7 papers
- Rice UniversityUS7 papers
- Chinese Academy of SciencesCN6 papers
- Louisiana State UniversityUS6 papers
- Temple UniversityUS6 papers
- University of California, IrvineUS6 papers
- Max Planck Institute for the Physics of Complex SystemsDE5 papers
- The Ohio State UniversityUS5 papers
- University of Maryland, College ParkUS5 papers
9 papers · 1 filter
Telecom-band Hyperentangled Photon Pairs from a Fiber-based Source
Changjia Chen, Calvin Xu, Arash Riazi +6
Hyperentanglement, the simultaneous and independent entanglement of quantum particles in multiple degrees of freedom, is a powerful resource that can be harnessed for efficient qua…
Minimum-Complexity Graph Simplification under Fréchet-Like Distances
Omrit Filtser, Majid Mirzanezhad, Carola Wenk
Simplifying graphs is a very applicable problem in numerous domains, especially in computational geometry. Given a geometric graph and a threshold, the minimum-complexity graph sim…
Non-Uniqueness of Non-Linear Optical Response
Gerard McCaul, Alexander F. King, Denys I. Bondar
In recent years, non-linear optical phenomena have attracted much attention, with a particular focus on the engineering and exploitation of non-linear responses. Comparatively litt…
Making Human-Like Trade-offs in Constrained Environments by Learning from Demonstrations
Arie Glazier, Andrea Loreggia, Nicholas Mattei +3
Many real-life scenarios require humans to make difficult trade-offs: do we always follow all the traffic rules or do we violate the speed limit in an emergency? These scenarios fo…
Quantum CPOs
Andre Kornell, Bert Lindenhovius, Michael Mislove
We introduce the monoidal closed category qCPO of quantum cpos, whose objects are "quantized" analogs of omega-complete partial orders (cpos). The category qCPO is enriched over th…
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning
Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen +1
Unsupervised domain adaptation (UDA) enables cross-domain learning without target domain labels by transferring knowledge from a labeled source domain whose distribution differs fr…