A Study of AI Population Dynamics with Million-agent Reinforcement Learning

We conduct an empirical study on discovering the ordered collective dynamics obtained by a population of intelligence agents, driven by million-agent reinforcement learning. Our intention is to put intelligent agents into a simulated natural context and verify if the principles developed in the real world could also be used in understanding an artificially-created intelligent population. To achieve this, we simulate a large-scale predator-prey world, where the laws of the world are designed by only the findings or logical equivalence that have been discovered in nature. We endow the agents with the intelligence based on deep reinforcement learning (DRL). In order to scale the population size up to millions agents, a large-scale DRL training platform with redesigned experience buffer is proposed. Our results show that the population dynamics of AI agents, driven only by each agent's individual self-interest, reveals an ordered pattern that is similar to the Lotka-Volterra model studied in population biology. We further discover the emergent behaviors of collective adaptations in studying how the agents' grouping behaviors will change with the environmental resources. Both of the two findings could be explained by the self-organization theory in nature.Comment: Full version of the paper presented at AAMAS 2018 (International Conference on Autonomous Agents and Multiagent Systems

INTUITIVE DECISION THEORY ANALYSIS AND THE EVALUATION MODEL

Intuitive decision-making studies the decision-maker’s decision-making behavior from the perspective of image thinking, which it poses a challenge to the classic decision-making hypothesis pursuing “optimal decision” because the outcomes of intuitive decision-making are difficulty to measure and its process isn’t easy to describe and control. Therefore it has not drawn the experts’ attention. This paper tries to establish an evaluation model of the intuitive decision-making as to giving a direction and inspiration of the quantization of intuitive decision-making, based on the systematic analysis of the existing domestic and international theory of intuitive decision-making. Key words: Intuitive decision-making, Thinking in images, The evaluation mode

Integrable Open Spin Chains from Flavored ABJM Theory

We compute the two-loop anomalous dimension matrix in the scalar sector of planar ${\cal N}=3$ flavored ABJM theory. Using coordinate Bethe ansatz, we obtain the reflection matrix and confirm that the boundary Yang-Baxter equations are satisfied. This establishes the integrability of this theory in the scalar sector at the two-loop order.Comment: v2, 25 pages, 2 figures, minor corrections, references adde

Laser Intensity Noise Suppression for Preparing Audio-Frequency 795 nm Squeezed Vacuum State of Light at Rubidium D1 Line

Laser intensity noise suppression has essential effects on preparation and characterization of the audio-frequency squeezed vacuum state of light based on a sub-threshold optical parametric oscillator (OPO).We have implemented two feedback loops by using relevant acousto-optical modulators (AOM) to stabilize the intensity of 795-nm near infrared (NIR) fundamental laser and 397.5-nm ultraviolet (UV) laser generated by cavity-enhanced frequency doubling.Typical peak-to-peak laser intensity fluctuation with a bandwidth of $\sim10$ kHz in a half hour has been improved from $\pm7.45$$\%$ to $\pm0.06$$\%$ for 795-nm NIR laser beam, and from $\pm9.04$$\%$ to $\pm0.05$$\%$ for 397.5-nm UV laser beam, respectively. The squeezing level of the squeezed vacuum state at 795 nm prepared by the sub-threshold OPO with a PPKTP crystal has been improved from -3.3 to -4.0 dB around 3$\sim$9 kHz of audio analysis frequency range.Comment: 5 pages, 4 figure

Towards Adversarially Robust Continual Learning

Recent studies show that models trained by continual learning can achieve the comparable performances as the standard supervised learning and the learning flexibility of continual learning models enables their wide applications in the real world. Deep learning models, however, are shown to be vulnerable to adversarial attacks. Though there are many studies on the model robustness in the context of standard supervised learning, protecting continual learning from adversarial attacks has not yet been investigated. To fill in this research gap, we are the first to study adversarial robustness in continual learning and propose a novel method called \textbf{T}ask-\textbf{A}ware \textbf{B}oundary \textbf{A}ugmentation (TABA) to boost the robustness of continual learning models. With extensive experiments on CIFAR-10 and CIFAR-100, we show the efficacy of adversarial training and TABA in defending adversarial attacks.Comment: ICASSP 202

Renalase Deficiency in Heart Failure Model of Rats—A Potential Mechanism Underlying Circulating Norepinephrine Accumulation

BACKGROUND: Sympathetic overactivity and catecholamine accumulation are important characteristic findings in heart failure, which contribute to its pathophysiology. Here, we identify a potential mechanism underlying norepinephrine accumulation in a rat model of heart failure. METHODOLOGY/PRINCIPAL FINDINGS: Initially, we constructed a rat model of unilateral renal artery stenosis (n = 16) and found that the expression of renalase, a previously identified secreted amine oxidase, was markedly reduced in the ischemic compared to the non-ischemic kidney (protein: 0.295±0.085 versus 0.765±0.171, p<0.05). Subsequently, we utilized an isolated perfused rat kidney model to demonstrate that the clearance rate of norepinephrine decreased with reduction of perfusion flow. On the basis of these findings, we hypothesized the reduced renal blood supply which occurs in heart failure would result in impaired synthesis of renalase by the kidney and consequently reduced degradation of circulating norepinephrine. To verify this, we used a rat model of infarction-induced heart failure (n = 12 per group). In these rats, the flow velocity of renal artery, when measured at four weeks, is obviously lower in the operation group. Renal expression of renalase was reduced (protein: 0.476±0.043 for control, 0.248±0.029 for operation versus 0.636±0.151 for sham-operation) and this was associated with an increase in circulating norepinephrine (0.168±0.016 ng/mL for control, 0.203±0.019 ng/mL for operation versus 0.138±0.008 ng/mL for sham-operation). CONCLUSIONS/SIGNIFICANCE: Renalase expression is influenced by renal blood flow and impaired synthesis of renalase by the kidney may represent a potential mechanism underlying circulating norepinephrine accumulation in heart failure