Signatures of quantum chaos of Rydberg dressed bosons in a triple-well potential

We study signatures of quantum chaos in dynamics of Rydberg dressed bosonic atoms held in a one dimensional triple-well potential. Long-range nearest-neighbor and next-nearest-neighbor interactions, induced by laser dressing atoms to strongly interacting Rydberg states, affect drastically mean field and quantum many-body dynamics. By analyzing the mean field dynamics, classical chaos regions with positive and large Lyapunov exponents are identified as a function of the potential well tilting and dressed interactions. In the quantum regime, it is found that level statistics of the eigen-energies gains a Wigner-Dyson distribution when the Lyapunov exponents are large, giving rise to signatures of strong quantum chaos. We find that both the time averaged entanglement entropy and survival probability of the initial state have distinctively large values in the quantum chaos regime. We further show that population variances could be used as an indicator of the emergence of quantum chaos. This might provide a way to directly probe quantum chaotic dynamics through analyzing population dynamics in individual potential wells

IGN : Implicit Generative Networks

In this work, we build recent advances in distributional reinforcement learning to give a state-of-art distributional variant of the model based on the IQN. We achieve this by using the GAN model's generator and discriminator function with the quantile regression to approximate the full quantile value for the state-action return distribution. We demonstrate improved performance on our baseline dataset - 57 Atari 2600 games in the ALE. Also, we use our algorithm to show the state-of-art training performance of risk-sensitive policies in Atari games with the policy optimization and evaluation

Robust Natural Language Understanding with Residual Attention Debiasing

Natural language understanding (NLU) models often suffer from unintended dataset biases. Among bias mitigation methods, ensemble-based debiasing methods, especially product-of-experts (PoE), have stood out for their impressive empirical success. However, previous ensemble-based debiasing methods typically apply debiasing on top-level logits without directly addressing biased attention patterns. Attention serves as the main media of feature interaction and aggregation in PLMs and plays a crucial role in providing robust prediction. In this paper, we propose REsidual Attention Debiasing (READ), an end-to-end debiasing method that mitigates unintended biases from attention. Experiments on three NLU tasks show that READ significantly improves the performance of BERT-based models on OOD data with shortcuts removed, including +12.9% accuracy on HANS, +11.0% accuracy on FEVER-Symmetric, and +2.7% F1 on PAWS. Detailed analyses demonstrate the crucial role of unbiased attention in robust NLU models and that READ effectively mitigates biases in attention. Code is available at https://github.com/luka-group/READ.Comment: ACL 2023 Finding

Synthesis and antibacterial activity evaluation of aminoguanidine or dihydrotriazine derivatives

In the alarming context of rising bacterial antibiotic resistance, there is an urgent need to discover new antibiotics or increase and/or enlarge the activity of those currently in use. In this article, aminoguanidine and dihydrotriazine derivatives were designed, synthesized and evaluated in terms of their antibacterial and antifungal activities. Most of the synthesized compounds showed potent inhibitory activities against different bacteria and one fungus with minimum inhibitory concentrations (MICs) ranging from 1 to 64 μg/mL, which obviously better than the positives control drug. The compound 23a showed the best antibacterial activities, whose MIC value was 1 μg/mL against eight strains. The cytotoxic activity of the compound 4c, 8a and 23a were assessed in Human liver cancer cells. The preliminary docking results imply that compounds 21b and 23a possibly display their antibacterial activity through the interaction with DHFR protein by targeting residues of the active cavities of DHFR

Synthesis and antibacterial activity evaluation of aminoguanidine or dihydrotriazine derivatives

301-308In the alarming context of rising bacterial antibiotic resistance, there is an urgent need to discover new antibiotics or increase and/or enlarge the activity of those currently in use. In this article, aminoguanidine and dihydrotriazine derivatives were designed, synthesized and evaluated in terms of their antibacterial and antifungal activities. Most of the synthesized compounds showed potent inhibitory activities against different bacteria and one fungus with minimum inhibitory concentrations (MICs) ranging from 1 to 64 μg/mL, which obviously better than the positives control drug. The compound 23a showed the best antibacterial activities, whose MIC value was 1 μg/mL against eight strains. The cytotoxic activity of the compound 4c, 8a and 23a were assessed in Human liver cancer cells. The preliminary docking results imply that compounds 21b and 23a possibly display their antibacterial activity through the interaction with DHFR protein by targeting residues of the active cavities of DHFR