Acceleration of Levenberg-Marquardt Training of Neural Networks with Variable Decay Rate

In the application of the standard Levenherg-Marquardt training process of neural networks, error oscillations are frequently observed and they usually aggravate on approaching the required accuracy. In this paper, a modified Levenberg-Marquardt method based on variable decay rate in each iteration is proposed in order to reduce such error oscillations. Through a certain variation of the decay rate, the time required for training of neural networks is cut down to less than half of that required in the standard Levenberg-Marquardt method. Several numerical examples are given to show the effectiveness of the proposed method.published_or_final_versio

Nuclear-localized focal adhesion kinase regulates inflammatory VCAM-1 expression.

Vascular cell adhesion molecule-1 (VCAM-1) plays important roles in development and inflammation. Tumor necrosis factor-α (TNF-α) and focal adhesion kinase (FAK) are key regulators of inflammatory and integrin-matrix signaling, respectively. Integrin costimulatory signals modulate inflammatory gene expression, but the important control points between these pathways remain unresolved. We report that pharmacological FAK inhibition prevented TNF-α-induced VCAM-1 expression within heart vessel-associated endothelial cells in vivo, and genetic or pharmacological FAK inhibition blocked VCAM-1 expression during development. FAK signaling facilitated TNF-α-induced, mitogen-activated protein kinase activation, and, surprisingly, FAK inhibition resulted in the loss of the GATA4 transcription factor required for TNF-α-induced VCAM-1 production. FAK inhibition also triggered FAK nuclear localization. In the nucleus, the FAK-FERM (band 4.1, ezrin, radixin, moesin homology) domain bound directly to GATA4 and enhanced its CHIP (C terminus of Hsp70-interacting protein) E3 ligase-dependent polyubiquitination and degradation. These studies reveal new developmental and anti-inflammatory roles for kinase-inhibited FAK in limiting VCAM-1 production via nuclear localization and promotion of GATA4 turnover

Jet Substructure Without Trees

We present an alternative approach to identifying and characterizing jet substructure. An angular correlation function is introduced that can be used to extract angular and mass scales within a jet without reference to a clustering algorithm. This procedure gives rise to a number of useful jet observables. As an application, we construct a top quark tagging algorithm that is competitive with existing methods.Comment: 22 pages, 16 figures, version accepted by JHE

Jet Dipolarity: Top Tagging with Color Flow

A new jet observable, dipolarity, is introduced that can distinguish whether a pair of subjets arises from a color singlet source. This observable is incorporated into the HEPTopTagger and is shown to improve discrimination between top jets and QCD jets for moderate to high pT.Comment: 8 pages, 6 figures (updated to JHEP version

Risk-Averse Matchings over Uncertain Graph Databases

A large number of applications such as querying sensor networks, and analyzing protein-protein interaction (PPI) networks, rely on mining uncertain graph and hypergraph databases. In this work we study the following problem: given an uncertain, weighted (hyper)graph, how can we efficiently find a (hyper)matching with high expected reward, and low risk? This problem naturally arises in the context of several important applications, such as online dating, kidney exchanges, and team formation. We introduce a novel formulation for finding matchings with maximum expected reward and bounded risk under a general model of uncertain weighted (hyper)graphs that we introduce in this work. Our model generalizes probabilistic models used in prior work, and captures both continuous and discrete probability distributions, thus allowing to handle privacy related applications that inject appropriately distributed noise to (hyper)edge weights. Given that our optimization problem is NP-hard, we turn our attention to designing efficient approximation algorithms. For the case of uncertain weighted graphs, we provide a $\frac{1}{3}$-approximation algorithm, and a $\frac{1}{5}$-approximation algorithm with near optimal run time. For the case of uncertain weighted hypergraphs, we provide a $\Omega(\frac{1}{k})$-approximation algorithm, where $k$ is the rank of the hypergraph (i.e., any hyperedge includes at most $k$ nodes), that runs in almost (modulo log factors) linear time. We complement our theoretical results by testing our approximation algorithms on a wide variety of synthetic experiments, where we observe in a controlled setting interesting findings on the trade-off between reward, and risk. We also provide an application of our formulation for providing recommendations of teams that are likely to collaborate, and have high impact.Comment: 25 page

Identifying Boosted Objects with N-subjettiness

We introduce a new jet shape -- N-subjettiness -- designed to identify boosted hadronically-decaying objects like electroweak bosons and top quarks. Combined with a jet invariant mass cut, N-subjettiness is an effective discriminating variable for tagging boosted objects and rejecting the background of QCD jets with large invariant mass. In efficiency studies of boosted W bosons and top quarks, we find tagging efficiencies of 30% are achievable with fake rates of 1%. We also consider the discovery potential for new heavy resonances that decay to pairs of boosted objects, and find significant improvements are possible using N-subjettiness. In this way, N-subjettiness combines the advantages of jet shapes with the discriminating power seen in previous jet substructure algorithms.Comment: 26 pages, 26 figures, 2 tables; v2: references added; v3: discussion of results extende

Packing density improvement through addition of limestone fines, superfine cement and condensed silica fume

Adoption of a low water/powder (W/P) ratio is the key to improve the strength and durability of concrete, which relies on a high packing density because fresh concrete requires excess water to offer flowability. To obtain a high packing density, powders with different particle sizes, including limestone fines (LSF), superfine cement (SFC), condensed silica fume (CSF), were added to the cement paste and the resulting packing densities were measured directly by a newly-developed wet packing test. Results demonstrated that addition of powders with a finer size would more significantly improve the packing density but the powders should be at least as fine as 1/4 of the OPC to effectively improve the packing density. Packing density and voids ratio relationship showed that a small increase in packing density can significantly decrease the voids ratio, which could allow the W/P ratio to be reduced to improve the strength and durability of the concrete without compromising the flowability.published_or_final_versio

Use of Limestone Fines to Reduce Permeability of Concrete for Durability Improvement

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