FENDI: High-Fidelity Entanglement Distribution in the Quantum Internet

A quantum network distributes quantum entanglements between remote nodes, which is key to many quantum applications. However, unavoidable noise in quantum operations could lead to both low throughput and low quality of entanglement distribution. This paper aims to address the simultaneous exponential degradation in throughput and quality in a buffered multi-hop quantum network. Based on an end-to-end fidelity model with worst-case (isotropic) noise, we formulate the high-fidelity remote entanglement distribution problem for a single source-destination pair, and prove its NP-hardness. To address the problem, we develop a fully polynomial-time approximation scheme for the control plane of the quantum network, and a distributed data plane protocol that achieves the desired long-term throughput and worst-case fidelity based on control plane outputs. To evaluate our algorithm and protocol, we develop a discrete-time quantum network simulator. Simulation results show the superior performance of our approach compared to existing fidelity-agnostic and fidelity-aware solutions

Untangling the chemical evolution of Titan's atmosphere and surface–from homogeneous to heterogeneous chemistry

The arrival of the Cassini-Huygens probe at Saturn's moon Titan - the only Solar System body besides Earth and Venus with a solid surface and a thick atmosphere with a pressure of 1.4 atm at surface level - in 2004 opened up a new chapter in the history of Solar System exploration. The mission revealed Titan as a world with striking Earth-like landscapes involving hydrocarbon lakes and seas as well as sand dunes and lava-like features interspersed with craters and icy mountains of hitherto unknown chemical composition. The discovery of a dynamic atmosphere and active weather system illustrates further the similarities between Titan and Earth. The aerosol-based haze layers, which give Titan its orange-brownish color, are not only Titan's most prominent optically visible features, but also play a crucial role in determining Titan's thermal structure and chemistry. These smog-like haze layers are thought to be very similar to those that were present in Earth's atmosphere before life developed more than 3.8 billion years ago, absorbing the destructive ultraviolet radiation from the Sun, thus acting as 'prebiotic ozone' to preserve astrobiologically important molecules on Titan. Compared to Earth, Titan's low surface temperature of 94 K and the absence of liquid water preclude the evolution of biological chemistry as we know it. Exactly because of these low temperatures, Titan provides us with a unique prebiotic 'atmospheric laboratory' yielding vital clues - at the frozen stage - on the likely chemical composition of the atmosphere of the primitive Earth. However, the underlying chemical processes, which initiate the haze formation from simple molecules, have been not understood well to date

Identifying Nuclear Receptor Ligands through Sequence-Based Deep Learning

This project focuses on developing machine learning methods to predict protein-ligand interactions. The unique proteins under study are nuclear receptors (NRs), which regulate hormone-triggered gene transcription and are often drug targets in cancer therapy. Compared to other proteins, the categorical labels for their ligand interactions are much more complex to annotate and learn in the framework of machine learning. The project aims at identifying ligands for NRs through sequence-based deep learning while addressing aforementioned challenges. The main contributions of this project include the following. (1) Data curation: Identification and curation of databases were performed. A rule was set up to deal with the complicated categorical labels for NR-ligand pairs. (2) Machine Learning Models: Shallow models, two-step deep model, and jointly trained deep model were trained. (3) Stratified Validation Sets: They were developed to tune the hyper-parameter of the model and improve model generalizability. (4) Transfer Learning: It was applied to tune models trained on other NRs so that novel ligands can be identified for orphan NRs. Specifically, categorical labels were first collected and curated for the identified data sets to enable model training and testing. Protein and ligand features were extracted by a pre-trained recurrent neural network (RNN) encoder using unlabeled data and then fed to various downstream supervised models, shallow or deep, for multi-class classification. Among shallow supervised models random forest showed the best results. For deep supervised models, a convolutional neural network (CNN) was trained subsequently or jointly with RNN. Comparisons between various shallow and deep models showed that although the way to train deep models, separately or jointly, did not make significant difference in model performance, there was an obvious improvement from shallow to deep models. Moreover, a stratified validation strategy was developed to further improve the generalizability of the model from the training set to test sets. Lastly, considering the very different distributions of biological features between training NRs and orphan NRs, transfer learning strategy was used to fine tune the model and improve the performance of ligand identification for two orphan NRs. Future plan includes the exploration of mutational effect, that is, the change in predicted label upon amino-acid substitutions, insertions, or deletions in NRs

Identifying Nuclear Receptor Ligands through Sequence-Based Deep Learning

This project focuses on developing machine learning methods to predict protein-ligand interactions. The unique proteins under study are nuclear receptors (NRs), which regulate hormone-triggered gene transcription and are often drug targets in cancer therapy. Compared to other proteins, the categorical labels for their ligand interactions are much more complex to annotate and learn in the framework of machine learning. The project aims at identifying ligands for NRs through sequence-based deep learning while addressing aforementioned challenges. The main contributions of this project include the following. (1) Data curation: Identification and curation of databases were performed. A rule was set up to deal with the complicated categorical labels for NR-ligand pairs. (2) Machine Learning Models: Shallow models, two-step deep model, and jointly trained deep model were trained. (3) Stratified Validation Sets: They were developed to tune the hyper-parameter of the model and improve model generalizability. (4) Transfer Learning: It was applied to tune models trained on other NRs so that novel ligands can be identified for orphan NRs. Specifically, categorical labels were first collected and curated for the identified data sets to enable model training and testing. Protein and ligand features were extracted by a pre-trained recurrent neural network (RNN) encoder using unlabeled data and then fed to various downstream supervised models, shallow or deep, for multi-class classification. Among shallow supervised models random forest showed the best results. For deep supervised models, a convolutional neural network (CNN) was trained subsequently or jointly with RNN. Comparisons between various shallow and deep models showed that although the way to train deep models, separately or jointly, did not make significant difference in model performance, there was an obvious improvement from shallow to deep models. Moreover, a stratified validation strategy was developed to further improve the generalizability of the model from the training set to test sets. Lastly, considering the very different distributions of biological features between training NRs and orphan NRs, transfer learning strategy was used to fine tune the model and improve the performance of ligand identification for two orphan NRs. Future plan includes the exploration of mutational effect, that is, the change in predicted label upon amino-acid substitutions, insertions, or deletions in NRs