238 research outputs found
Sampling Algorithms for Butterfly Counting on Temporal Bipartite Graphs
Temporal bipartite graphs are widely used to denote time-evolving
relationships between two disjoint sets of nodes, such as customer-product
interactions in E-commerce and user-group memberships in social networks.
Temporal butterflies, -bicliques that occur within a short period and in
a prescribed order, are essential in modeling the structural and sequential
patterns of such graphs. Counting the number of temporal butterflies is thus a
fundamental task in analyzing temporal bipartite graphs. However, existing
algorithms for butterfly counting on static bipartite graphs and motif counting
on temporal unipartite graphs are inefficient for this purpose. In this paper,
we present a general framework with three sampling strategies for temporal
butterfly counting. Since exact counting can be time-consuming on large graphs,
our approach alternatively computes approximate estimates accurately and
efficiently. We also provide analytical bounds on the number of samples each
strategy requires to obtain estimates with small relative errors and high
probability. We finally evaluate our framework on six real-world datasets and
demonstrate its superior accuracy and efficiency compared to several baselines.
Overall, our proposed framework and sampling strategies provide efficient and
accurate approaches to approximating temporal butterfly counts on large-scale
temporal bipartite graphs.Comment: 10 pages, 10 figures; under revie
Protective effect of salvianolic acid B against intestinal ischemia reperfusion-induced injury in a rat model
Purpose: To evaluate the gastro-protective efficacy of salvianolic acid B (SAB) against intestinal ischemic-reperfusion injury (IIRI) in a rat model.Methods: Forty-eight healthy male rats were randomly choosen and divided into 4 groups of 12 rats each. Control group rats underwent laparotomy without occlusion; IIRI group rats underwent laparotomy with occlusion for 60 min, followed by 24 h of reperfusion; SAB + IIRI group received 7 days of pretreatment with 40 mg/kg of SAB + IIRI; while the fourth group received only SAB. The antioxidant, inflammatory markers, intestinal permeability marker, as well as intestinal histopathological changes were assessed.Results: The activities of antioxidants including reduced glutathione (GSH), catalase (CAT) and superoxide dismutase (SOD) were significantly ameliorated (p < 0.01) in SAB-supplemented group (SAB + IIRI). The concentration of inflammatory markers, including interleukin-1β (IL-1β), interleukin-6 (IL-6), tumor necrosis factor alpha (TNF-ι) and nuclear factor p65 (NF-p65) as well as small intestinal permeability marker (FITC-Dextran), were significantly reduced (p < 0.01) following administration of SAB for 7 days. In addition, pretreatment with SAB reverted intestinal (ileum) histopathological changes to almost normal architecture with significant reduction in Chiu score.Conclusion: The results of this study demonstrate that SAB may protect the intestine by attenuating oxidative stress and inflammatory response and hence, may be potentially for treating IIRI.Keywords: Salvianolic acid B, Intestinal Ischemia-reperfusion, Antioxidants, Inflammation, Intestinal permeabilit
Efficient and Scalable Parametric High-Order Portfolios Design via the Skew-t Distribution
Since Markowitz's mean-variance framework, optimizing a portfolio that
maximizes the profit and minimizes the risk has been ubiquitous in the
financial industry. Initially, profit and risk were measured by the first two
moments of the portfolio's return, a.k.a. the mean and variance, which are
sufficient to characterize a Gaussian distribution. However, it is broadly
believed that the first two moments are not enough to capture the
characteristics of the returns' behavior, which have been recognized to be
asymmetric and heavy-tailed. Although there is ample evidence that portfolio
designs involving the third and fourth moments, i.e., skewness and kurtosis,
will outperform the conventional mean-variance framework, they are non-trivial.
Specifically, in the classical framework, the memory and computational cost of
computing the skewness and kurtosis grow sharply with the number of assets. To
alleviate the difficulty in high-dimensional problems, we consider an
alternative expression for high-order moments based on parametric
representations via a generalized hyperbolic skew-t distribution. Then, we
reformulate the high-order portfolio optimization problem as a fixed-point
problem and propose a robust fixed-point acceleration algorithm that solves the
problem in an efficient and scalable manner. Empirical experiments also
demonstrate that our proposed high-order portfolio optimization framework is of
low complexity and significantly outperforms the state-of-the-art methods by 2
to 4 orders of magnitude
Thermomechanical response of metallic sandwich tubes with prismatic cores considering active cooling
Network Topology Inference with Sparsity and Laplacian Constraints
We tackle the network topology inference problem by utilizing Laplacian
constrained Gaussian graphical models, which recast the task as estimating a
precision matrix in the form of a graph Laplacian. Recent research
\cite{ying2020nonconvex} has uncovered the limitations of the widely used
-norm in learning sparse graphs under this model: empirically, the
number of nonzero entries in the solution grows with the regularization
parameter of the -norm; theoretically, a large regularization parameter
leads to a fully connected (densest) graph. To overcome these challenges, we
propose a graph Laplacian estimation method incorporating the -norm
constraint. An efficient gradient projection algorithm is developed to solve
the resulting optimization problem, characterized by sparsity and Laplacian
constraints. Through numerical experiments with synthetic and financial
time-series datasets, we demonstrate the effectiveness of the proposed method
in network topology inference
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SelfâHealable and Recyclable Tactile Force Sensors with PostâTunable Sensitivity
It is challenging to postâtune the sensitivity of a tactile force sensor. Herein, a facile method is reported to tailor the sensing properties of conductive polymer composites by utilizing the liquidâlike property of dynamic polymer matrix at low strain rates. The idea is demonstrated using dynamic polymer composites (CB/dPDMS) made via evaporationâinduced gelation of the suspending toluene solution of carbon black (CB) and acidâcatalyzed dynamic polydimethylsiloxane (dPDMS). The dPDMS matrices allow CB to redistribute to change the sensitivity of materials at the liquidâlike state, but exhibit typical solidâlike behavior and thus can be used as strain sensors at normal strain rates. It is shown that the gauge factor of the polymer composites can be easily postâtuned from 1.4 to 51.5. In addition, the dynamic polymer matrices also endow the composites with interesting selfâhealing ability and recyclability. Therefore, it is envisioned that this method can be useful in the design of various novel tactile sensing materials for many applications
Revealing Hidden Vibration Polariton Interactions by 2D IR Spectroscopy
We report the first experimental two-dimensional infrared (2D IR) spectra of
novel molecular photonic excitations - vibrational-polaritons. The application
of advanced 2D IR spectroscopy onto novel vibrational-polariton challenges and
advances our understanding in both fields. From spectroscopy aspect, 2D IR
spectra of polaritons differ drastically from free uncoupled molecules; from
vibrational-polariton aspects, 2D IR uniquely resolves hybrid light-matter
polariton excitations and unexpected dark states in a state-selective manner
and revealed hidden interactions between them. Moreover, 2D IR signals
highlight the role of vibrational anharmonicities in generating non-linear
signals. To further advance our knowledge on 2D IR of vibrational polaritons,
we develop a new quantum-mechanical model incorporating the effects of both
nuclear and electrical anharmonicities on vibrational-polaritons and their 2D
IR signals. This work reveals polariton physics that is difficult or impossible
to probe with traditional linear spectroscopy and lays the foundation for
investigating new non-linear optics and chemistry of molecular
vibrational-polaritons
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