Stent intimal hyperplasia leads to in stent restenosis and thrombosis. This study determined whether Fibulin-1 activity in smooth muscle cells (SMCs) contributes to stent restenosis or thrombosis.
...Stent implantation was conducted in a pig model. Target vessel samples were stained and analyzed by protein mass spectrometry. Cell experiments and Fibulin-1 SMC specific knockout mice (Fbln1SMKO) were used to investigate the mechanism of Fibulin-1 induced SMC proliferation and thrombosis.
SMC proliferation and phenotypic transition are the main pathological changes of intimal hyperplasia in venous stents. Protein mass spectrometry analysis revealed a total of 67 upregulated proteins and 39 downregulated proteins in intimal hyperplasia after stent implantation compared with normal iliac vein tissues. Among them, Fibulin-1 ranked among the top proteins altered. Fibulin-1 overexpressing human SMCs (Fibulin-1-hSMCs) showed increased migration and phenotypic switching from contractile to secretory type and Fibulin-1 inhibition decreased the activity of SMCs. Mechanistically, Fibulin-1-hSMCs displayed increased levels of angiotensin converting enzyme (ACE) expression and angiotensin II signaling. Inhibition of ACE or angiotensin II signaling alleviated the migration of Fibulin-1-hSMCs. Using Fibulin-1 SMC specific knockout mice (Fbln1SMKO) and venous thrombosis model, we demonstrated that Fibulin-1 deletion attenuated intimal SMCs proliferation and thrombosis. Further, Fibulin-1 concentration was high in iliac vein compression syndrome (IVCS) patients treated with stent and was an independent predictor of venous insufficiency.
Fibulin-1 promotes SMC proliferation partially through ACE secretion and angiotensin II signaling after stent implantation. Fibulin-1 plays a role in venous insufficiency syndrome, implicating the protein in the detection and treatment of IVCS.
This paper studies the disturbance attenuation problem of a class of nonlinear systems in feedforward form that is subject to both dynamic uncertainty and disturbance. When the disturbance vanishes, ...the equilibrium of the closed-loop system is globally asymptotically stable. Two versions of small gain theorem with restrictions are employed to establish the global attractiveness and local stability of the closed-loop system at the equilibrium respectively.
This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader ...is led step by step into understanding of a novel paradigm that leverages the power of machine learning without losing sight of the system-theoretical principles of black-box identification. The authors’ reformulation of the identification problem in the light of regularization theory not only offers new insight on classical questions, but paves the way to new and powerful algorithms for a variety of linear and nonlinear problems. Regression methods such as regularization networks and support vector machines are the basis of techniques that extend the function-estimation problem to the estimation of dynamic models. Many examples, also from real-world applications, illustrate the comparative advantages of the new nonparametric approach with respect to classic parametric prediction error methods. The challenges it addresses lie at the intersection of several disciplines so Regularized System Identification will be of interest to a variety of researchers and practitioners in the areas of control systems, machine learning, statistics, and data science. This is an open access book.
In computer architecture research and development, simulation is a powerful way of acquiring and predicting processor behaviors. While architectural simulation has been extensively utilized for ...computer performance evaluation, design space exploration, and computer architecture assessment, it still suffers from the high computational costs in practice. Specifically, the total simulation time is determined by the simulator's raw speed and the total number of simulated instructions. The simulator's speed can be improved by enhanced simulation infrastructures (e.g., simulators with high-level abstraction, parallel simulators, and hardware-assisted simulators). Orthogonal to these work, recent studies also managed to significantly reduce the total number of simulated instructions with a slight loss of accuracy. Interestingly, we observe that most of these work are built upon statistical techniques. This survey presents a comprehensive review to such studies and proposes a taxonomy based on the sources of reduction. In addition to identifying the similarities and differences of state-of-the-art approaches, we further discuss insights gained from these studies as well as implications for future research.
In this paper, we study the global robust stabilization problem of strict feedforward systems subject to input unmodeled dynamics. We present a recursive design method for a nested saturation ...controller which globally stabilizes the closed-loop system in the presence of input unmodeled dynamics. One of the difficulties of the problem is that the Jacobian linearization of our system at the origin may not be stabilizable. We overcome this difficulty by employing a special version of the small gain theorem to address the local stability, and, respectively, the asymptotic small gain theorem to establish the global convergence property, of the closed-loop system. An example is given to show that a redesign of the controller is required to guarantee the global robust asymptotic stability in the presence of the input unmodeled dynamics.
Kernel-based regularized system identification is one of the major advances in system identification in the past decade. A recent focus is to develop its asymptotic theory and it has been found that ...the Stein's unbiased risk estimator is asymptotically optimal (AO) in the sense of minimizing the mean squared error for prediction ability, but the empirical Bayes estimator is not AO in general. In this article, we further study the AO of various cross-validation (CV) estimators and show that the generalized CV method, leave <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula>-out CV method, and <inline-formula><tex-math notation="LaTeX">r</tex-math></inline-formula>-fold CV method are all AO under mild assumptions, but the hold out CV method is not AO in general. We illustrate the efficacy of our theoretical results through numerical simulations.
As a fundamental task in computer architecture research, performance comparison has been continuously hampered by the variability of computer performance. In traditional performance comparisons, the ...impact of performance variability is usually ignored (i.e., the means of performance observations are compared regardless of the variability), or in the few cases directly addressed with i-statistics without checking the number and normality of performance observations. In this paper, we formulate a performance comparison as a statistical task, and empirically illustrate why and how common practices can lead to incorrect comparisons. We propose a non-parametric hierarchical performance testing (HPT) framework for performance comparison, which is significantly more practical than standard i-statistics because it does not require to collect a large number of performance observations in order to achieve a normal distribution of sample mean. In particular, the proposed HPT can facilitate quantitative performance comparison, in which the performance speedup of one computer over another is statistically evaluated. Compared with the HPT, a common practice which uses geometric mean performance scores to estimate the performance speedup has errors of 8.0 to 56.3 percent on SPEC CPU2006 or SPEC MPI2007, which demonstrates the necessity of using appropriate statistical techniques. This HPT framework has been implemented as an open-source software, and integrated in the PARSEC 3.0 benchmark suite.
Through one decade’s development, the kernel-based regularization method (KRM) has become a complement to the classical maximum likelihood/prediction error method and an emerging new system ...identification paradigm. One recent example is its application in the non-causal system identification, and the key issue lies in the design and analysis of kernels for non-causal systems. In this paper, we develop systematic ways to deal with this issue. In particular, we first introduce the guidelines for kernel design and then extend the system theoretic framework to design the so-called non-causal simulation-induced (NCSI) kernel, and we also study its structural properties, including stability and semiseparability. Finally, we consider some special cases of the NCSI kernel and show their advantage over the existing kernels through numerical simulations.
IMR: High-Performance Low-Cost Multi-Ring NoCs Shaoli Liu; Tianshi Chen; Ling Li ...
IEEE transactions on parallel and distributed systems,
2016-June-1, 2016-6-1, 20160601, Volume:
27, Issue:
6
Journal Article
Peer reviewed
A ring topology is a common solution of network-on-chip (NoC) in industry, but is frequently criticized to have poor scalability. In this paper, we present a novel type of multi-ring NoC called ...isolated multi-ring (IMR), which can even support chip multiprocessors (CMPs) with 1,024 cores. In IMR, any pair of cores are connected via at least one isolated ring, so that each packet can reach the destination without transferring from one ring to another. Therefore, IMR no longer needs expensive routers as mesh, which not only enhances the network performance but also reduces hardware overheads. We utilize simulated evolution to design optimized IMR topologies. We compare these IMR topologies against nine representative NoCs (e.g., traditional mesh, multi mesh, low-cost mesh, Express-virtual-channels mesh (EVC), torus ring, and hierarchical ring). We observe from experiments that IMR significantly outperforms its competitors in both saturation throughput and latency across all scenarios considered. For example, in a 16 × 16 CMP, IMR improves the saturation throughput of a state-of-the-art mesh (EVC) by 265.29 percent on average, and reduces the average packet latency on SPLASH-2 application traces by 71.58 percent, while consuming 5.08 percent less area and 9.76 percent less power. In a 32 × 32 CMP, IMR averagely improves the saturation throughput of EVC by 191.58 percent, and averagely reduces the packet latency on SPLASH-2 application traces by 23.09 percent, while consuming 2.86 percent less area and 10.81 percent less power.
Machine-learning techniques have recently been proved to be successful in various domains, especially in emerging commercial applications. As a set of machine- learning techniques, artificial neural ...networks （ANNs）, requiring considerable amount of computation and memory, are one of the most popular algorithms and have been applied in a broad range of applications such as speech recognition, face identification, natural language processing, ect. Conventionally, as a straightforward way, conventional CPUs and GPUs are energy-inefficient due to their excessive effort for flexibility. According to the aforementioned situation, in recent years, many researchers have proposed a number of neural network accelerators to achieve high performance and low power consumption. Thus, the main purpose of this literature is to briefly review recent related works, as well as the DianNao-family accelerators. In summary, this review can serve as a reference for hardware researchers in the area of neural networks.
