Physical implementation of holonomic quantum computation in decoherence-free subspaces with trapped ions

We propose a feasible scheme to achieve holonomic quantum computation in a decoherence-free subspace (DFS) with trapped ions. By the application of appropriate bichromatic laser fields on the designated ions, we are able to construct two noncommutable single-qubit gates and one controlled-phase gate using the holonomic scenario in the encoded DFS.Comment: 4 pages, 3 figures. To appear in Phys. Rev. A 74 (2006

A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games

This paper proposes novel, end-to-end deep reinforcement learning algorithms for learning two-player zero-sum Markov games. Our objective is to find the Nash Equilibrium policies, which are free from exploitation by adversarial opponents. Distinct from prior efforts on finding Nash equilibria in extensive-form games such as Poker, which feature tree-structured transition dynamics and discrete state space, this paper focuses on Markov games with general transition dynamics and continuous state space. We propose (1) Nash DQN algorithm, which integrates DQN with a Nash finding subroutine for the joint value functions; and (2) Nash DQN Exploiter algorithm, which additionally adopts an exploiter for guiding agent's exploration. Our algorithms are the practical variants of theoretical algorithms which are guaranteed to converge to Nash equilibria in the basic tabular setting. Experimental evaluation on both tabular examples and two-player Atari games demonstrates the robustness of the proposed algorithms against adversarial opponents, as well as their advantageous performance over existing methods

Generalized Category Discovery with Decoupled Prototypical Network

Generalized Category Discovery (GCD) aims to recognize both known and novel categories from a set of unlabeled data, based on another dataset labeled with only known categories. Without considering differences between known and novel categories, current methods learn about them in a coupled manner, which can hurt model's generalization and discriminative ability. Furthermore, the coupled training approach prevents these models transferring category-specific knowledge explicitly from labeled data to unlabeled data, which can lose high-level semantic information and impair model performance. To mitigate above limitations, we present a novel model called Decoupled Prototypical Network (DPN). By formulating a bipartite matching problem for category prototypes, DPN can not only decouple known and novel categories to achieve different training targets effectively, but also align known categories in labeled and unlabeled data to transfer category-specific knowledge explicitly and capture high-level semantics. Furthermore, DPN can learn more discriminative features for both known and novel categories through our proposed Semantic-aware Prototypical Learning (SPL). Besides capturing meaningful semantic information, SPL can also alleviate the noise of hard pseudo labels through semantic-weighted soft assignment. Extensive experiments show that DPN outperforms state-of-the-art models by a large margin on all evaluation metrics across multiple benchmark datasets. Code and data are available at https://github.com/Lackel/DPN.Comment: Accepted by AAAI 202

An interferon regulatory factor-like binding element restricts Xmyf-5 expression in the posterior somites during Xenopus myogenesis

AbstractThe expression of myf-5, a key component of myogenic regulatory genes, declines progressively in mature somitic cells during vertebrate myogenesis. Little is known about how this down-regulation takes place. Here we provide evidence that an interferon regulatory factor binding element (IRF element) within the Xenopus myf-5 promoter is responsible for the elimination of myf-5 transcription in mature somitic mesoderm of Xenopus embryos. We show that this IRF element mediates the down-regulation of Xmyf-5 transcription in gastrula embryos, and can specifically interact with nuclear proteins of early neurula. Moreover, deletion of this IRF element results in the anterior expansion of reporter gene transcripts within somitic mesoderm in transgenic embryos. Our results, therefore, provide insight into how the negative control of Xmyf-5 expression takes place

Towards Self-Interpretable Graph-Level Anomaly Detection

Graph-level anomaly detection (GLAD) aims to identify graphs that exhibit notable dissimilarity compared to the majority in a collection. However, current works primarily focus on evaluating graph-level abnormality while failing to provide meaningful explanations for the predictions, which largely limits their reliability and application scope. In this paper, we investigate a new challenging problem, explainable GLAD, where the learning objective is to predict the abnormality of each graph sample with corresponding explanations, i.e., the vital subgraph that leads to the predictions. To address this challenging problem, we propose a Self-Interpretable Graph aNomaly dETection model (SIGNET for short) that detects anomalous graphs as well as generates informative explanations simultaneously. Specifically, we first introduce the multi-view subgraph information bottleneck (MSIB) framework, serving as the design basis of our self-interpretable GLAD approach. This way SIGNET is able to not only measure the abnormality of each graph based on cross-view mutual information but also provide informative graph rationales by extracting bottleneck subgraphs from the input graph and its dual hypergraph in a self-supervised way. Extensive experiments on 16 datasets demonstrate the anomaly detection capability and self-interpretability of SIGNET.Comment: 23 pages; accepted to NeurIPS 202

Investigation of thermal management for lithium-ion pouch battery module based on phase change slurry and mini channel cooling plate

In this paper, the thermal management based on phase change slurry (PCS) and mini channel cooling plate for the lithium-ion pouch battery module was proposed. The three-dimensional thermal model was established and the optimum structure of the cooling plate with mini channel was designed with the orthogonal matrix experimental method to balance the cooling performance and energy consumption. The simulation results showed that the cooling performance of PCS consisting of 20% n-octadecane microcapsules and 80% water was better than that of pure water, glycol solution and mineral oil, when the mass flow rate was less than 3 x 10(-4) kg s(-1). For different concentrations of PCS, if the mass flow rate exceeded the critical value, its cooling performance was worse than that of pure water. When the cooling target for battery maximum temperature was higher than 309 K, the PCS cooling with appropriate microcapsule concentration had the edge over in energy consumption compared with water cooling. At last, the dimensionless empirical formula was obtained to predict the effect of the PCS's physical parameters and flow characteristics on the heat transfer and cooling performance. The simulation results will be useful for the design of PCS based battery thermal management systems. (C) 2018 Elsevier Ltd. All rights reserved