Methods for Signal Filtering and Modelling and Their Parallel Distributed Computing Implementation

In this thesis the problem of filtering and modelling one-dimensional discrete signals and implementation of corresponding parallel distributed algorithms will be addressed. In Chapter 2, the research areas of parallel distributed computing environments, rank-based nonlinear filter and fractal functions are reviewed. In Chapter 3, an Interactive Parallel Distributed Computing Environment (IPDCE) is implemented based on Parallel Virtual Machine (PVM) and an interactive application development tool, the Tc1 language. The approach we use is to provide a Tc1 version interface for all procedures of the PVM interface library so that users can utilize any PVM procedure to do their parallel computing interactively. In Chapter 4, an interactive parallel stack-filtering system is implemented, based on the IPDCE. The user can play with this filtering system in both traditional command mode and modern Graphics User Interface (GUI) mode. In order to reduce the time required to compute a standard stack filter, a new minimum threshold decomposition scheme is introduced and other techniques such as minimizing the number of logical operations and utilizing the CPU bit-fields parallel property are also suggested. In this filtering system the user can select sequential or parallel stack-filtering algorithms. The parallel distributed stack-filtering algorithm is implemented with equal task partitioning and PVM. Two numerical simulations show that the interactive parallel stack-filtering system is efficient for both the sequential and the parallel filtering algorithms. In Chapter 5, an extended Iterated Function System (IFS) interpolation method is introduced for modelling a given discrete signal. In order to get the solution of the inverse IFS problem in reasonable time, a suboptimal search algorithm, which estimates first the local self-affine region and then the map parameters is suggested, and the neighbourhood information of a self-affine region is used for enhancing the robustness of this suboptimal algorithm. The parallel distributed version of the in-verse IFS algorithm is implemented with equal task partitioning and using a Remote Procedure Call application programming interface library. The numerical simulation results show that the IFS approach achieves a higher signal to noise ratio than does an existing approach based on autoregressive modelling for self-affine and approximately self-affine one-dimensional signals and, when the number of computers is small, the speed-up ratio is almost linear. In Chapter 6, inverse IFS interpolation is introduced to model self-affine and approximately self-affine one-dimensional signals corrupted by Gaussian noise. Local cross-validation is applied for compromising between the degree of smoothness and fidelity to the data. The parallel distributed version of the inverse algorithm is implemented in Parallel Virtual Machine (PVM) with static optimal task partitioning. A simple computing model is applied which partitions tasks based on only each computer's capability. Several numerical simulation results show that the new IFS inverse algorithm achieves a higher signal to noise ratio than does existing autoregressive modelling for noisy self-affine or approximately self-affine signals.- There is little machine idle time relative to computing time in the optimal task partition mode. In Chapter 7, local IFS interpolation, which realises the IFS limit for self-affine data, is applied to model non self-affi.ne signals. It is difficult, however, to explore the whole parameter space to achieve globally optimal parameter estimation. A two-stage search scheme is suggested to estimate the self-affine region and the associated region parameters so that a suboptimal solution can be obtained in reasonable time. In the first stage, we calculate the self-affine region under the condition that the associated region length is twice that of the self-affine region. Then the second stage calculates the associated region for each self-affine region using a full search space. In order to combat the performance degradation caused by the the difference of machines capabilities and unpredictable external loads, a dynamic load-balance technique based on a data parallelism scheme is applied in the parallel distributed version of the inverse local IFS algorithm. Some numerical simulations show that our inverse local IFS algorithm works efficiently for several types of one-dimensional signal, and the parallel version with dynamic load balance can automatically ensure that each machine is busy with computing and with low idle time

Quantifying evolution of soot mixing state from transboundary transport of biomass burning emissions

Incomplete combustion of fossil fuels and biomass burning emit large amounts of soot particles into the troposphere. The condensation process is considered to influence the size (Dp) and mixing state of soot particles, which affects their solar absorption efficiency and lifetimes. However, quantifying aging evolution of soot remains hampered in the real world because of complicated sources and observation technologies. In the Himalayas, we isolated soot sourced from transboundary transport of biomass burning and revealed soot aging mechanisms through microscopic observations. Most of coated soot particles stabilized one soot core under Dp &lt; 400 nm, but 34.8% of them contained multi-soot cores (nsoot ≥ 2) and nsoot increased 3–9 times with increasing Dp. We established the soot mixing models to quantify transformation from condensation- to coagulation-dominant regime at Dp ≈ 400 nm. Studies provide essential references for adopting mixing rules and quantifying the optical absorption of soot in atmospheric models.</p

Category-Specific CNN for Visual-aware CTR Prediction at JD.com

As one of the largest B2C e-commerce platforms in China, JD com also powers a leading advertising system, serving millions of advertisers with fingertip connection to hundreds of millions of customers. In our system, as well as most e-commerce scenarios, ads are displayed with images.This makes visual-aware Click Through Rate (CTR) prediction of crucial importance to both business effectiveness and user experience. Existing algorithms usually extract visual features using off-the-shelf Convolutional Neural Networks (CNNs) and late fuse the visual and non-visual features for the finally predicted CTR. Despite being extensively studied, this field still face two key challenges. First, although encouraging progress has been made in offline studies, applying CNNs in real systems remains non-trivial, due to the strict requirements for efficient end-to-end training and low-latency online serving. Second, the off-the-shelf CNNs and late fusion architectures are suboptimal. Specifically, off-the-shelf CNNs were designed for classification thus never take categories as input features. While in e-commerce, categories are precisely labeled and contain abundant visual priors that will help the visual modeling. Unaware of the ad category, these CNNs may extract some unnecessary category-unrelated features, wasting CNN's limited expression ability. To overcome the two challenges, we propose Category-specific CNN (CSCNN) specially for CTR prediction. CSCNN early incorporates the category knowledge with a light-weighted attention-module on each convolutional layer. This enables CSCNN to extract expressive category-specific visual patterns that benefit the CTR prediction. Offline experiments on benchmark and a 10 billion scale real production dataset from JD, together with an Online A/B test show that CSCNN outperforms all compared state-of-the-art algorithms

More Severe Manifestations and Poorer Short-Term Prognosis of Ganglioside-Associated Guillain-Barré Syndrome in Northeast China

Ganglioside as a neurotrophic drug has been hitherto widely used in China, although Guillain-Barré syndrome (GBS) following intravenous ganglioside treatment was reported in Europe several decades ago. We identified 7 patients who developed GBS after intravenous use of gangliosides (ganglioside+ group) and compared their clinical data with those of 77 non-ganglioside-associated GBS patients (ganglioside− group) in 2013, aiming at gaining the distinct features of ganglioside-associated GBS. Although the mean age, protein levels in cerebrospinal fluid (CSF) and frequency of cranial nerve involvement were similar between the two groups, the Hughes Functional Grading Scale (HFGS) score and the Medical Research Council (MRC) sum score at nadir significantly differed (4.9±0.4 vs 3.6±1.0; 7.7±5.5 vs 36.9±14.5, both p<0.001), indicating a higher disease severity of ganglioside-associated GBS. A higher ratio of patients with ganglioside-associated GBS required mechanical ventilation (85.7% vs 15.6%, p<0.01). The short-term prognosis of ganglioside-associated GBS, as measured by the HFGS score and the MRC sum score at discharge, was poorer (4.3±0.5 vs 2.8±1.1; 17.3±12.9 vs 46.0±13.9, both p<0.001). All the patients in the ganglioside+ group presented an axonal form of GBS, namely acute motor axonal neuropathy (AMAN). When compared with the AMAN patients in the ganglioside− group, more severe functional deficits at nadir and poorer recovery after standard treatment were still prominent in ganglioside-associated GBS. Anti-GM1 and anti-GT1a antibodies were detectable in patients with AMAN while not in patients with the demyelinating subtype of GBS. The concentrations of these antibodies in patients with AMAN were insignificantly different between the ganglioside+ and ganglioside− groups. In sum, ganglioside-associated GBS may be a devastating side effect of intravenous use of gangliosides, which usually manifests a more severe clinical course and poorer outcome

Predictive Value of Serum Uric Acid in Perioperative Acute Ischemic Stroke in Patients with Non-small Cell Lung Cancer

Background Perioperative acute ischemic stroke (POAIS) is a severe complication of surgery, which can increase surgical mortality and reduce patients&apos; quality of life. The pathogeneses are complex and rarely explored, especially in patients with non-small cell lung cancer (NSCLC) . Objective To investigate the influencing factors of POAIS in NSCLC patients and the predictive value of serum uric acid (SUA) on the occurrence of POAIS in NSCLC patients. Methods A total of 25 NSCLC patients admitted to the Fourth Hospital of Hebei Medical University from July 2014 to April 2022, who suffered from POAIS following lung resection were selected as the case group, while 126 patients without POAIS were randomly selected as the control group after matching by age and gender. The preoperative baseline data, intraoperative data and postoperative pathology-related data of all patients were collected. Multivariate Logistic regression analysis was performed to explore the influencing factors of POAIS in the NSCLC patients, and the receiver operating characteristic (ROC) curve was plotted to evaluate the predictive value of preoperative SUA on the development of POAIS in NSCLC patients. Results The average age of the 151 patients was (64±7) years, 57.62% (87/151) of whom were male. The multivariate Logistic regression analysis showed that SUA was an influencing factor of POAIS in NSCLC patients〔OR=0.990, 95%CI (0.982, 0.998) , P=0.019〕. The ROC curve indicated that the area under the curve (AUC) of SUA to predict POAIS in NSCLC patients was 0.64, with an optimal threshold value of 307.40 &#x03BC;mol/L, sensitivity and specificity of 58.7% and 76.0%, respectively. Conclusion Preoperative SUA level can serve as an independent predictor of POAIS incidence in NSCLC patients. Higher SUA levels at baseline may predict a lower risk of POAIS

Implicit smoothed particle hydrodynamics model for simulating incompressible fluid-elastic coupling

Fluid simulation has been one of the most critical topics in computer graphics for its capacity to produce visually realistic effects. The intricacy of fluid simulation manifests most with interacting dynamic elements. The coupling for such scenarios has always been challenging to manage due to the numerical instability arising from the coupling boundary between different elements. Therefore, we propose an implicit smoothed particle hydrodynamics fluid-elastic coupling approach to reduce the instability issue for fluid-fluid, fluid-elastic, and elastic-elastic coupling circumstances. By deriving the relationship between the universal pressure field with the incompressible attribute of the fluid, we apply the number density scheme to solve the pressure Poisson equation for both fluid and elastic material to avoid the density error for multi-material coupling and conserve the non-penetration condition for elastic objects interacting with fluid particles. Experiments show that our method can effectively handle the multiphase fluids simulation with elastic objects under various physical properties

FGF10 Protects Against Renal Ischemia/Reperfusion Injury by Regulating Autophagy and Inflammatory Signaling

Ischemia-reperfusion (I/R) is a common cause of acute kidney injury (AKI), which is associated with high mortality and poor outcomes. Autophagy plays important roles in the homeostasis of renal tubular cells (RTCs) and is implicated in the pathogenesis of AKI, although its role in the process is complex and controversial. Fibroblast growth factor 10 (FGF10), a multifunctional FGF family member, was reported to exert protective effect against cerebral ischemia injury and myocardial damage. Whether FGF10 has similar beneficial effect, and if so whether autophagy is associated with the potential protective activity against AKI has not been investigated. Herein, we report that FGF10 treatment improved renal function and histological integrity in a rat model of renal I/R injury. We observed that FGF10 efficiently reduced I/R-induced elevation in blood urea nitrogen, serum creatinine as well as apoptosis induction of RTCs. Interestingly, autophagy activation following I/R was suppressed by FGF10 treatment based on the immunohistochemistry staining and immunoblot analyses of LC3, Beclin-1 and SQSTM1/p62. Moreover, combined treatment of FGF10 with Rapamycin partially reversed the renoprotective effect of FGF10 suggesting the involvement of mTOR pathway in the process. Interestingly, FGF10 also inhibited the release of HMGB1 from the nucleus to the extracellular domain and regulated the expression of inflammatory cytokines such as TNF-α, IL-1β and IL-6. Together, these results indicate that FGF10 could alleviate kidney I/R injury by suppressing excessive autophagy and inhibiting inflammatory response and may therefore have the potential to be used for the prevention and perhaps treatment of I/R-associated AKI