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 740$-$860 km s$^{-1}$. 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