1,821 research outputs found
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)
- …