828 research outputs found
Comparative Synthesis: Learning Near-Optimal Network Designs by Query
When managing wide-area networks, network architects must decide how to
balance multiple conflicting metrics, and ensure fair allocations to competing
traffic while prioritizing critical traffic. The state of practice poses
challenges since architects must precisely encode their intent into formal
optimization models using abstract notions such as utility functions, and
ad-hoc manually tuned knobs. In this paper, we present the first effort to
synthesize optimal network designs with indeterminate objectives using an
interactive program-synthesis-based approach. We make three contributions.
First, we present comparative synthesis, an interactive synthesis framework
which produces near-optimal programs (network designs) through two kinds of
queries (Propose and Compare), without an objective explicitly given. Second,
we develop the first learning algorithm for comparative synthesis in which a
voting-guided learner picks the most informative query in each iteration. We
present theoretical analysis of the convergence rate of the algorithm. Third,
we implemented Net10Q, a system based on our approach, and demonstrate its
effectiveness on four real-world network case studies using black-box oracles
and simulation experiments, as well as a pilot user study comprising network
researchers and practitioners. Both theoretical and experimental results show
the promise of our approach
First detection of transverse vertical oscillation during the expansion of coronal loops
In this Letter, we perform a detailed analysis of the M5.5-class eruptive
flare occurring in active region 12929 on 2022 January 20. The eruption of a
hot channel generates a fast coronal mass ejection (CME) and a dome-shaped
extreme-ultraviolet (EUV) wave at speeds of 740860 km s. The CME is
associated with a type II radio burst, implying that the EUV wave is a
fast-mode shock wave. During the impulsive phase, the flare shows
quasi-periodic pulsations (QPPs) in EUV, hard X-ray, and radio wavelengths. The
periods of QPPs range from 18 s to 113 s, indicating that flare energy is
released and nonthermal electrons are accelerated intermittently with multiple
time scales. The interaction between the EUV wave and low-lying adjacent
coronal loops (ACLs) results in contraction, expansion, and transverse vertical
oscillation of ACLs. The speed of contraction in 171, 193, and 211 {\AA} is
higher than that in 304 {\AA}. The periods of oscillation are 253 s and 275 s
in 304 {\AA} and 171 {\AA}, respectively. A new scenario is proposed to explain
the interaction. The equation that interprets the contraction and oscillation
of the overlying coronal loops above a flare core can also interpret the
expansion and oscillation of ACLs, suggesting that the two phenomena are the
same in essence.Comment: 11 pages, 12 figures, accepted for publication in ApJ
catena-Poly[[bis(pyridine-κN)zinc(II)]-μ-benzene-1,4-dicarboxylato-κ2 O 1:O 4]
In the title coordination polymer, [Zn(C8H4O4)(C5H5N)2]n, the ZnII atom, located on a twofold rotation axis, is tetracoordinated by two monodentate O atoms from two different carboxylate groups and two pyridyl N atoms, forming a distorted tetrahedral geometry. The ZnII atoms are bridged by terephthalate ligands, generating an infinite zigzag chain along [101]
Contextual Dictionary Lookup for Knowledge Graph Completion
Knowledge graph completion (KGC) aims to solve the incompleteness of
knowledge graphs (KGs) by predicting missing links from known triples, numbers
of knowledge graph embedding (KGE) models have been proposed to perform KGC by
learning embeddings. Nevertheless, most existing embedding models map each
relation into a unique vector, overlooking the specific fine-grained semantics
of them under different entities. Additionally, the few available fine-grained
semantic models rely on clustering algorithms, resulting in limited performance
and applicability due to the cumbersome two-stage training process. In this
paper, we present a novel method utilizing contextual dictionary lookup,
enabling conventional embedding models to learn fine-grained semantics of
relations in an end-to-end manner. More specifically, we represent each
relation using a dictionary that contains multiple latent semantics. The
composition of a given entity and the dictionary's central semantics serves as
the context for generating a lookup, thus determining the fine-grained
semantics of the relation adaptively. The proposed loss function optimizes both
the central and fine-grained semantics simultaneously to ensure their semantic
consistency. Besides, we introduce two metrics to assess the validity and
accuracy of the dictionary lookup operation. We extend several KGE models with
the method, resulting in substantial performance improvements on widely-used
benchmark datasets
LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity
Heterophily has been considered as an issue that hurts the performance of
Graph Neural Networks (GNNs). To address this issue, some existing work uses a
graph-level weighted fusion of the information of multi-hop neighbors to
include more nodes with homophily. However, the heterophily might differ among
nodes, which requires to consider the local topology. Motivated by it, we
propose to use the local similarity (LocalSim) to learn node-level weighted
fusion, which can also serve as a plug-and-play module. For better fusion, we
propose a novel and efficient Initial Residual Difference Connection (IRDC) to
extract more informative multi-hop information. Moreover, we provide
theoretical analysis on the effectiveness of LocalSim representing node
homophily on synthetic graphs. Extensive evaluations over real benchmark
datasets show that our proposed method, namely Local Similarity Graph Neural
Network (LSGNN), can offer comparable or superior state-of-the-art performance
on both homophilic and heterophilic graphs. Meanwhile, the plug-and-play model
can significantly boost the performance of existing GNNs. Our code is provided
at https://github.com/draym28/LSGNN.Comment: The first two authors contributed equally to this work; IJCAI2
Size-dependent spin-reorientation transition in Nd2Fe14B nanoparticles
Nd2Fe14B magnetic nanoparticles have been successfully produced using a
surfactant-assisted ball milling technique. The nanoparticles with different
size about 6, 20 and 300 nm were obtained by a size-selection process.
Spin-reorientation transition temperature of the NdFeB nanoparticles was then
determined by measuring the temperature dependence of DC and AC magnetic
susceptibility. It was found that the spin-reorientation transition temperature
(Tsr) of the nanoparticles is strongly size dependent, i.e., Tsr of the 300 nm
particles is lower than that of raw materials and a significant decrease was
observed in the 20 nm particles
Alpha Lipoic Acid Modulated High Glucose-Induced Rat Mesangial Cell Dysfunction via mTOR/p70S6K/4E-BP1 Pathway
The aim of this study was to investigate whether alpha lipoic acid (LA) regulates high glucose-induced mesangial cell proliferation and extracellular matrix production via mTOR/p70S6K/4E-BP1 signaling. The effect of LA on high glucose-induced cell proliferation, fibronectin (FN), and collagen type I (collagen-I) expression and its mechanisms were examined in cultured rat mesangial cells by methylthiazol tetrazolium (MTT) assay, flow cytometry, ELISA assay, and western blot, respectively. LA at a relatively low concentration (0.25 mmol/L) acted as a growth factor in rat mesangial cells, promoted entry of cell cycle into S phase, extracellular matrix formation, and phosphorylated AKT, mTOR, p70S6K, and 4E-BP1. These effects disappeared when AKT expression was downregulated with PI3K/AKT inhibitor LY294002. Conversely, LA at a higher concentration (1.0 mmol/L) inhibited high glucose-induced rat mesangial cell proliferation, entry of cell cycle into S phase, and extracellular matrix exertion, as well as phosphorylation of mTOR, p70S6K, and 4E-BP1 but enhanced the activity of AMPK. However, these effects disappeared when AMPK activity was inhibited with CaMKK inhibitor STO-609. These results suggest that LA dose-dependently regulates mesangial cell proliferation and matrix protein secretion by mTOR/p70S6K/4E-BP1 signaling pathway under high glucose conditions
Poly[[μ-aqua-triaqua[μ6-1,3,4,6-tetrakis(carboxylatomethyl)-7,8-diphenylglycoluril]dizinc] monohydrate]
In the crystal structure of the title coordination polymer, {[Zn2(C24H18N4O10)(H2O)4]·H2O}n, the molecular building block (MBB), viz [Zn2(CO2)4(H2O)4], comprises two ZnII cations, each bridged by three carboxylate groups from different ligand molecules. These two ZnII cations exhibit different coordination environments: a distorted trigonal–pyramidal coordination, as an intermediate, is formed by the two coordinated water molecules and three carboxylate groups, and a distorted octahedral geometry defined by three water molecules and three carboxylate groups, in which two carboxylate groups from the same side of the clip glycoluril ring and one water molecule are bidentate bridging, whereas others are monodentate units. Every ligand molecule connects four MBBs, thus forming a three-dimensional structure. Extensive intra- and intermolecular O—H⋯O hydrogen bonding is observed
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