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  1. Laser cooling is a key ingredient for quantum control of atomic systems in a variety of settings. In divalent atoms, two-stage Doppler cooling is typically used to bring atoms to the uK regime. Here, we implement a pulsed radial cooling scheme using the ultranarrow 1S0-3P0 clock transition in ytterbium to realize sub-recoil temperatures, down to tens of nK. Together with sideband cooling along the one-dimensional lattice axis, we efficiently prepare atoms in shallow lattices at an energy of 6 lattice recoils. Under these conditions key limits on lattice clock accuracy and instability are reduced, opening the door to dramatic improvements.more »Furthermore, tunneling shifts in the shallow lattice do not compromise clock accuracy at the 10-19 level.« less
    Free, publicly-accessible full text available July 1, 2023
  2. Free, publicly-accessible full text available June 1, 2023
  3. Free, publicly-accessible full text available August 19, 2022
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  5. Recent work on Question Answering (QA) and Conversational QA (ConvQA) emphasizes the role of retrieval: a system first retrieves evidence from a large collection and then extracts answers. This open-retrieval setting typically assumes that each question is answerable by a single span of text within a particular passage (a span answer). The supervision signal is thus derived from whether or not the system can recover an exact match of this ground-truth answer span from the retrieved passages. This method is referred to as span-match weak supervision. However, information-seeking conversations are challenging for this span-match method since long answers, especially freeformmore »answers, are not necessarily strict spans of any passage. Therefore, we introduce a learned weak supervision approach that can identify a paraphrased span of the known answer in a passage. Our experiments on QuAC and CoQA datasets show that although a span-match weak supervisor can handle conversations with span answers, it is not sufficient for freeform answers generated by people. We further demonstrate that our method is more flexible since it can handle both span answers and freeform answers. In particular, our method outperforms the span-match method on conversations with freeform answers, and it can be more powerful when combined with the span-match method. We also conduct in-depth analyses to show more insights on open-retrieval ConvQA under a weak supervision setting.« less
  6. ABSTRACT Of all the factors that influence star formation, magnetic fields are perhaps the least well understood. The goal of this paper is to characterize the 3D magnetic field properties of nearby molecular clouds through various methods of statistically analysing maps of polarized dust emission. Our study focuses on nine clouds, with data taken from the Planck Sky Survey as well as data from the Balloon-borne Large Aperture Submillimeter Telescope for Polarimetry observations of Vela C. We compare the distributions of polarization fraction (p), dispersion in polarization angles ($\mathcal {S}$), and hydrogen column density (NH) for each of our targeted clouds.more »To broaden the scope of our analysis, we compare the distributions of our clouds’ polarization observables with measurements from synthetic polarization maps generated from numerical simulations. We also use the distribution of polarization fraction measurements to estimate the inclination angle of each cloud’s cloud-scale magnetic field. We obtain a range of inclination angles associated with our clouds, varying from 16○ to 69○. We establish inverse correlations between p and both $\mathcal {S}$ and NH in almost every cloud, but we are unable to establish a statistically robust $\mathcal {S}$ versus NH trend. By comparing the results of these different statistical analysis techniques, we are able to propose a more comprehensive view of each cloud’s 3D magnetic field properties. These detailed cloud analyses will be useful in the continued studies of cloud-scale magnetic fields and the ways in which they affect star formation within these molecular clouds.« less
  7. Federated learning allows multiple users to collaboratively train a shared classifica- tion model while preserving data privacy. This approach, where model updates are aggregated by a central server, was shown to be vulnerable to poisoning backdoor attacks: a malicious user can alter the shared model to arbitrarily classify specific inputs from a given class. In this paper, we analyze the effects of backdoor attacks on federated meta-learning, where users train a model that can be adapted to dif- ferent sets of output classes using only a few examples. While the ability to adapt could, in principle, make federated learning frameworksmore »more robust to backdoor attacks (when new training examples are benign), we find that even 1-shot attacks can be very successful and persist after additional training. To address these vulner- abilities, we propose a defense mechanism inspired by matching networks, where the class of an input is predicted from the similarity of its features with a support set of labeled examples. By removing the decision logic from the model shared with the federation, success and persistence of backdoor attacks are greatly reduced.« less