Interpretable Sequence Classification Via Prototype Trajectory

We propose a novel interpretable recurrent neural network (RNN) model, called ProtoryNet, in which we introduce a new concept of prototype trajectories. Motivated by the prototype theory in modern linguistics, ProtoryNet makes a prediction by finding the most similar prototype for each sentence in a text sequence and feeding an RNN backbone with the proximity of each of the sentences to the prototypes. The RNN backbone then captures the temporal pattern of the prototypes, to which we refer as prototype trajectories. The prototype trajectories enable intuitive, fine-grained interpretation of how the model reached to the final prediction, resembling the process of how humans analyze paragraphs. Experiments conducted on multiple public data sets reveal that the proposed method not only is more interpretable but also is more accurate than the current state-of-the-art prototype-based method. Furthermore, we report a survey result indicating that human users find ProtoryNet more intuitive and easier to understand, compared to the other prototype-based methods

Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization

We propose a federated learning method with weighted nodes in which the weights can be modified to optimize the model's performance on a separate validation set. The problem is formulated as a bilevel optimization where the inner problem is a federated learning problem with weighted nodes and the outer problem focuses on optimizing the weights based on the validation performance of the model returned from the inner problem. A communication-efficient federated optimization algorithm is designed to solve this bilevel optimization problem. Under an error-bound assumption, we analyze the generalization performance of the output model and identify scenarios when our method is in theory superior to training a model only locally and to federated learning with static and evenly distributed weights

Error Compensation of Single-Qubit Gates in a Surface Electrode Ion Trap Using Composite Pulses

The fidelity of laser-driven quantum logic operations on trapped ion qubits tend to be lower than microwave-driven logic operations due to the difficulty of stabilizing the driving fields at the ion location. Through stabilization of the driving optical fields and use of composite pulse sequences, we demonstrate high fidelity single-qubit gates for the hyperfine qubit of a $^{171}\text{Yb}^+$ ion trapped in a microfabricated surface electrode ion trap. Gate error is characterized using a randomized benchmarking protocol, and an average error per randomized Clifford group gate of $3.6(3)\times10^{-4}$ is measured. We also report experimental realization of palindromic pulse sequences that scale efficiently in sequence length

Biochemical Characterization of Enzyme Fidelity of Influenza A Virus RNA Polymerase Complex

It is widely accepted that the highly error prone replication process of influenza A virus (IAV), together with viral genome assortment, facilitates the efficient evolutionary capacity of IAV. Therefore, it has been logically assumed that the enzyme responsible for viral RNA replication process, influenza virus type A RNA polymerase (IAV Pol), is a highly error-prone polymerase which provides the genomic mutations necessary for viral evolution and host adaptation. Importantly, however, the actual enzyme fidelity of IAV RNA polymerase has never been characterized.Here we established new biochemical assay conditions that enabled us to assess both polymerase activity with physiological NTP pools and enzyme fidelity of IAV Pol. We report that IAV Pol displays highly active RNA-dependent RNA polymerase activity at unbiased physiological NTP substrate concentrations. With this robust enzyme activity, for the first time, we were able to compare the enzyme fidelity of IAV Pol complex with that of bacterial phage T7 RNA polymerase and the reverse transcriptases (RT) of human immunodeficiency virus (HIV-1) and murine leukemia virus (MuLV), which are known to be low and high fidelity enzymes, respectively. We observed that IAV Pol displayed significantly higher fidelity than HIV-1 RT and T7 RNA polymerase and equivalent or higher fidelity than MuLV RT. In addition, the IAV Pol complex showed increased fidelity at lower temperatures. Moreover, upon replacement of Mg(++) with Mn(++), IAV Pol displayed increased polymerase activity, but with significantly reduced processivity, and misincorporation was slightly elevated in the presence of Mn(++). Finally, when the IAV nucleoprotein (NP) was included in the reactions, the IAV Pol complex exhibited enhanced polymerase activity with increased fidelity.Our study indicates that IAV Pol is a high fidelity enzyme. We envision that the high fidelity nature of IAV Pol may be important to counter-balance the multiple rounds of IAV genome amplification per infection cycle, which provides IAV Pol with ample opportunities to generate and amplify genomic founder mutations, and thus achieve optimal viral mutagenesis for its evolution