Multi-Agent Deep Reinforcement Learning for Request Dispatching in Distributed-Controller Software-Defined Networking

Recently, distributed controller architectures have been quickly gaining popularity in Software-Defined Networking (SDN). However, the use of distributed controllers introduces a new and important Request Dispatching (RD) problem with the goal for every SDN switch to properly dispatch their requests among all controllers so as to optimize network performance. This goal can be fulfilled by designing an RD policy to guide distribution of requests at each switch. In this paper, we propose a Multi-Agent Deep Reinforcement Learning (MA-DRL) approach to automatically design RD policies with high adaptability and performance. This is achieved through a new problem formulation in the form of a Multi-Agent Markov Decision Process (MA-MDP), a new adaptive RD policy design and a new MA-DRL algorithm called MA-PPO. Extensive simulation studies show that our MA-DRL technique can effectively train RD policies to significantly outperform man-made policies, model-based policies, as well as RD policies learned via single-agent DRL algorithms

Andreev and Single Particle Tunneling Spectroscopies in Underdoped Cuprates

We study tunneling spectroscopy between a normal metal and underdoped cuprate superconductor modeled by a phenomenological theory in which the pseudogap is a precursor to the undoped Mott insulator. In the transparent tunneling limit, the spectra show a small energy gap associated with Andreev reflection. In the Giaever limit, the spectra show a large energy gap associated with single particle tunneling. Our theory semi-quantitatively describes the two gap behavior observed in tunneling experiments.Comment: 5 pages, 4 figures, submitted to Phys. Rev. Lett. minor changes of reference

A strict test in climate modeling with spectrally resolved radiances: GCM simulation versus AIRS observations

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95152/1/grl23741.pd

Does Misclassifying Non-confounding Covariates as Confounders Affect the Causal Inference within the Potential Outcomes Framework?

The Potential Outcome Framework (POF) plays a prominent role in the field of causal inference. Most causal inference models based on the POF (CIMs-POF) are designed for eliminating confounding bias and default to an underlying assumption of Confounding Covariates. This assumption posits that the covariates consist solely of confounders. However, the assumption of Confounding Covariates is challenging to maintain in practice, particularly when dealing with high-dimensional covariates. While certain methods have been proposed to differentiate the distinct components of covariates prior to conducting causal inference, the consequences of treating non-confounding covariates as confounders remain unclear. This ambiguity poses a potential risk when conducting causal inference in practical scenarios. In this paper, we present a unified graphical framework for the CIMs-POF, which greatly enhances the comprehension of these models' underlying principles. Using this graphical framework, we quantitatively analyze the extent to which the inference performance of CIMs-POF is influenced when incorporating various types of non-confounding covariates, such as instrumental variables, mediators, colliders, and adjustment variables. The key findings are: in the task of eliminating confounding bias, the optimal scenario is for the covariates to exclusively encompass confounders; in the subsequent task of inferring counterfactual outcomes, the adjustment variables contribute to more accurate inferences. Furthermore, extensive experiments conducted on synthetic datasets consistently validate these theoretical conclusions.Comment: 12 pages, 4 figure

Bis(2-cyclo­hexyl­imino­methyl-4,6-disulfanylphenolato)nickel(II) acetonitrile solvate

In the title compound, [Ni(C13H16NOS2)2]·CH3CN, the NiII atom is four-coordinated by two N,O-bidentate Schiff base ligands, resulting in a distorted tetra­hedral coordination for the metal ion

Effects of limited moisture content and storing temperature on retrogradation of rice starch

The objective of this study is to investigate the effects of limited moisture content and storing temperature on the retrogradation of rice starch. Starch was gelatinized in various moisture contents (30–42%) and rice paste was stored at different temperatures (4 °C, 15 °C, 30 °C, −18/30 °C and 4/30 °C). X-ray diffraction (XRD) analysis revealed that after retrogradation, the crystalline type of rice starch changed from A-type to B + V type. The B-type crystallinity of retrograded rice starch under 30 °C was the highest among the five temperature conditions, and an increase in B-type crystallinity with increasing moisture content was observed. Differential scanning calorimetry (DSC) results revealed that rice starch retrogradation consists of recrystallization of amylopectin and amylose, and is mainly attributed to amylopectin. The higher moisture content was favorable for amylopectin recrystallization, whereas the moisture content had little effect on the amylose recrystallization. The optimal temperature for amylopectin and amylose recrystallization was 4 °C and 15 °C, respectively. The amylopectin recrystallization enthalpy of rice starch stored at 4/30 °C was mediated between 4 °C and 30 °C but always higher than that at −18/30 °C. On the whole, after being heated at 42% moisture content and stored at 4 °C, rice starch showed the maximum total retrogradation enthalpy (8.44 J/g)