3-(2-Amino­ethyl)-2-(4-chloro­anilino)­quinazolin-4(3H)-one methanol 0.75-solvate

In the asymmetric unit of the title compound, C16H15ClN4O·0.75CH3OH, there are two independent quinazolin-4(3H)-one mol­ecules and one and a half methanol mol­ecules. One of the methanol mol­ecules is disordered over two positions with equal occupancies. The dihedral angles between the quinazoline ring system and the chloro­benzene ring in the two quinazolin-4(3H)-one mol­ecules are essentially the same, at 39.83 (1) and 39.84 (1)°. Intra­molecular N—H⋯N and O—H⋯O, and inter­molecular N—H⋯O and N—H⋯N hydrogen bonds are observed. In addition, π–π stacking inter­actions, with centroid-to-centroid distances of 3.654 (1), 3.766 (1) and 3.767 (1) Å, and weak C—H⋯π inter­actions, are observed

E17110 promotes reverse cholesterol transport with liver X receptor β agonist activity in vitro

AbstractLiver X receptor (LXR) plays an important role in reverse cholesterol transport (RCT), and activation of LXR could reduce atherosclerosis. In the present study we used a cell-based screening method to identify new potential LXRβ agonists. A novel benzofuran-2-carboxylate derivative was identified with LXRβ agonist activity: E17110 showed a significant activation effect on LXRβ with an EC50 value of 0.72μmol/L. E17110 also increased the expression of ATP-binding cassette transporter A1 (ABCA1) and G1 (ABCG1) in RAW264.7 macrophages. Moreover, E17110 significantly reduced cellular lipid accumulation and promoted cholesterol efflux in RAW264.7 macrophages. Interestingly, we found that the key amino acids in the LXRβ ligand-binding domain had distinct interactions with E17110 as compared to TO901317. These results suggest that E17110 was identified as a novel compound with LXRβ agonist activity in vitro via screening, and could be developed as a potential anti-atherosclerotic lead compound

Realization of Two-Dimensional Spin-orbit Coupling for Bose-Einstein Condensates

Cold atoms with laser-induced spin-orbit (SO) interactions provide intriguing new platforms to explore novel quantum physics beyond natural conditions of solids. Recent experiments demonstrated the one-dimensional (1D) SO coupling for boson and fermion gases. However, realization of 2D SO interaction, a much more important task, remains very challenging. Here we propose and experimentally realize, for the first time, 2D SO coupling and topological band with $^{87}$Rb degenerate gas through a minimal optical Raman lattice scheme, without relying on phase locking or fine tuning of optical potentials. A controllable crossover between 2D and 1D SO couplings is studied, and the SO effects and nontrivial band topology are observed by measuring the atomic cloud distribution and spin texture in the momentum space. Our realization of 2D SO coupling with advantages of small heating and topological stability opens a broad avenue in cold atoms to study exotic quantum phases, including the highly-sought-after topological superfluid phases.Comment: 27 pages, 5 figure

Preparation and Properties of Fe3O4 Biomimetic Micro-nano Structure Coatings

Nanoparticles filling is one of the most effective methods to build the micro-nano structure. In this paper, the composite coatings containing Fe3O4 nanoparticles were prepared from fluorinated silicon polymer by in-situ polymerization. FT-IR was used to characterize the structure of the composite material. SEM and AFM were performed to observe the microstructure of the coatings. The contact angle of water and coatings was tested. The results showed that the biomimetic micro-nano structure of the coatings, which formed on the glass plate, was exactly familiar with that of the surface of lotus leaves. Keyword: micro-nano structure; Fe3O4 nanoparticles; in-situ polymerization; biomimeti

Demonstration of Adiabatic Variational Quantum Computing with a Superconducting Quantum Coprocessor

Adiabatic quantum computing enables the preparation of many-body ground states. This is key for applications in chemistry, materials science, and beyond. Realisation poses major experimental challenges: Direct analog implementation requires complex Hamiltonian engineering, while the digitised version needs deep quantum gate circuits. To bypass these obstacles, we suggest an adiabatic variational hybrid algorithm, which employs short quantum circuits and provides a systematic quantum adiabatic optimisation of the circuit parameters. The quantum adiabatic theorem promises not only the ground state but also that the excited eigenstates can be found. We report the first experimental demonstration that many-body eigenstates can be efficiently prepared by an adiabatic variational algorithm assisted with a multi-qubit superconducting coprocessor. We track the real-time evolution of the ground and exited states of transverse-field Ising spins with a fidelity up that can reach about 99%.Comment: 12 pages, 4 figure

Reward Imputation with Sketching for Contextual Batched Bandits

Contextual batched bandit (CBB) is a setting where a batch of rewards is observed from the environment at the end of each episode, but the rewards of the non-executed actions are unobserved, resulting in partial-information feedback. Existing approaches for CBB often ignore the rewards of the non-executed actions, leading to underutilization of feedback information. In this paper, we propose an efficient approach called Sketched Policy Updating with Imputed Rewards (SPUIR) that completes the unobserved rewards using sketching, which approximates the full-information feedbacks. We formulate reward imputation as an imputation regularized ridge regression problem that captures the feedback mechanisms of both executed and non-executed actions. To reduce time complexity, we solve the regression problem using randomized sketching. We prove that our approach achieves an instantaneous regret with controllable bias and smaller variance than approaches without reward imputation. Furthermore, our approach enjoys a sublinear regret bound against the optimal policy. We also present two extensions, a rate-scheduled version and a version for nonlinear rewards, making our approach more practical. Experimental results show that SPUIR outperforms state-of-the-art baselines on synthetic, public benchmark, and real-world datasets.Comment: Accepted by NeurIPS 202

Generative Retrieval with Semantic Tree-Structured Item Identifiers via Contrastive Learning

The retrieval phase is a vital component in recommendation systems, requiring the model to be effective and efficient. Recently, generative retrieval has become an emerging paradigm for document retrieval, showing notable performance. These methods enjoy merits like being end-to-end differentiable, suggesting their viability in recommendation. However, these methods fall short in efficiency and effectiveness for large-scale recommendations. To obtain efficiency and effectiveness, this paper introduces a generative retrieval framework, namely SEATER, which learns SEmAntic Tree-structured item identifiERs via contrastive learning. Specifically, we employ an encoder-decoder model to extract user interests from historical behaviors and retrieve candidates via tree-structured item identifiers. SEATER devises a balanced k-ary tree structure of item identifiers, allocating semantic space to each token individually. This strategy maintains semantic consistency within the same level, while distinct levels correlate to varying semantic granularities. This structure also maintains consistent and fast inference speed for all items. Considering the tree structure, SEATER learns identifier tokens' semantics, hierarchical relationships, and inter-token dependencies. To achieve this, we incorporate two contrastive learning tasks with the generation task to optimize both the model and identifiers. The infoNCE loss aligns the token embeddings based on their hierarchical positions. The triplet loss ranks similar identifiers in desired orders. In this way, SEATER achieves both efficiency and effectiveness. Extensive experiments on three public datasets and an industrial dataset have demonstrated that SEATER outperforms state-of-the-art models significantly.Comment: 8 main pages, 3 pages for appendi

Observation of quantum fingerprinting beating the classical limit

Quantum communication has historically been at the forefront of advancements, from fundamental tests of quantum physics to utilizing the quantum-mechanical properties of physical systems for practical applications. In the field of communication complexity, quantum communication allows the advantage of an exponential reduction in the information transmitted over classical communication to accomplish distributed computational tasks. However, to date, demonstrating this advantage in a practical setting continues to be a central challenge. Here, we report an experimental demonstration of a quantum fingerprinting protocol that for the first time surpasses the ultimate classical limit to transmitted information. Ultra-low noise superconducting single-photon detectors and a stable fibre-based Sagnac interferometer are used to implement a quantum fingerprinting system that is capable of transmitting less information than the classical proven lower bound over 20 km standard telecom fibre for input sizes of up to two Gbits. The results pave the way for experimentally exploring the advanced features of quantum communication and open a new window of opportunity for research in communication complexity and testing the foundations of physics.Comment: 19 pages, 4 figure