EFFICIENT APPROXIMATION FOR LARGE-SCALE KERNEL CLUSTERING ANALYSIS

Kernel k-means is useful for performing clustering on nonlinearly separable data. The kernel k-means is hard to scale to large data due to the quadratic complexity. In this paper, we propose an approach which utilizes the low-dimensional feature approximation of the Gaussian kernel function to capitalize a fast linear k-means solver to perform the nonlinear kernel k-means. This approach takes advantage of the efficiency of the linear solver and the nonlinear partitioning ability of the kernel clustering. The experimental results show that the proposed approach is much more efficient than a normal kernel k- means solver and achieves similar clustering performance

Integration of Social Media News Mining and Text Mining Techniques to Determine a Corporate’s Competitive Edge

Market globalization have triggered much more severe challenges for corporates than ever before. Thus, how to survive in this highly fluctuating economic atmosphere is an attractive topic for corporate managers, especially when an economy goes into a severe recession. One of the most consensus conclusions is to highly integrate a corporate’s supply chain network, as it can facilitate knowledge circulation, reduce transportation cost, increase market share, and sustain customer loyalty. However, a corporate’s supply chain relations are unapparent and opaque. To solve such an obstacle, this study integrates text mining (TM) and social network analysis (SNA) techniques to exploit the latent relation among corporates from social media news. Sequentially, this study examines its impact on corporate operating performance forecasting. The empirical result shows that the proposed mechanism is a promising alternative for performance forecasting. Public authorities and decision makers can thus consider the potential implications when forming a future policy

Microfluidic assisted synthesis of silver nanoparticle–chitosan composite microparticles for antibacterial applications

AbstractSilver nanoparticle (Ag NP)-loaded chitosan composites have numerous biomedical applications; however, fabricating uniform composite microparticles remains challenging. This paper presents a novel microfluidic approach for single-step and in situ synthesis of Ag NP-loaded chitosan microparticles. This proposed approach enables obtaining uniform and monodisperse Ag NP-loaded chitosan microparticles measuring several hundred micrometers. In addition, the diameter of the composites can be tuned by adjusting the flow on the microfluidic chip. The composite particles containing Ag NPs were characterized using UV–vis spectra and scanning electron microscopy-energy dispersive X-ray spectrometry data. The characteristic peaks of Ag NPs in the UV–vis spectra and the element mapping or pattern revealed the formation of nanosized silver particles. The results of antibacterial tests indicated that both chitosan and composite particles showed antibacterial ability, and Ag NPs could enhance the inhibition rate and exhibited dose-dependent antibacterial ability. Because of the properties of Ag NPs and chitosan, the synthesized composite microparticles can be used in several future potential applications, such as bactericidal agents for water disinfection, antipathogens, and surface plasma resonance enhancers

Association analyses of East Asian individuals and trans-ancestry analyses with European individuals reveal new loci associated with cholesterol and triglyceride levels

Large-scale meta-analyses of genome-wide association studies (GWAS) have identified >175 loci associated with fasting cholesterol levels, including total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG). With differences in linkage disequilibrium (LD) structure and allele frequencies between ancestry groups, studies in additional large samples may detect new associations. We conducted staged GWAS meta-analyses in up to 69,414 East Asian individuals from 24 studies with participants from Japan, the Philippines, Korea, China, Singapore, and Taiwan. These meta-analyses identified (P < 5 × 10-8) three novel loci associated with HDL-C near CD163-APOBEC1 (P = 7.4 × 10-9), NCOA2 (P = 1.6 × 10-8), and NID2-PTGDR (P = 4.2 × 10-8), and one novel locus associated with TG near WDR11-FGFR2 (P = 2.7 × 10-10). Conditional analyses identified a second signal near CD163-APOBEC1. We then combined results from the East Asian meta-analysis with association results from up to 187,365 European individuals from the Global Lipids Genetics Consortium in a trans-ancestry meta-analysis. This analysis identified (log10Bayes Factor ≥6.1) eight additional novel lipid loci. Among the twelve total loci identified, the index variants at eight loci have demonstrated at least nominal significance with other metabolic traits in prior studies, and two loci exhibited coincident eQTLs (P < 1 × 10-5) in subcutaneous adipose tissue for BPTF and PDGFC. Taken together, these analyses identified multiple novel lipid loci, providing new potential therapeutic targets

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P &lt; 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Efficient Data Classification with Privacy-Preservation

資料分類是被廣泛使用的資料探勘技術。資料分類從已標記的資料去學習出分類器，用以去預測無標記資料的可能標記。在資料分類演算法中，支持向量機器具有目前最佳的效能。資料隱私是使用資料探勘技巧的關鍵議題。在本論文中，我們研究如何在使用支持向量機器時達成對資料隱私的保護，並且探討如何有效率的產生支持向量機器分類器。 在當前的雲端運算趨勢中，運算外包漸為流行。因為支持向量機器的訓練演算法牽涉到大量的運算，將運算外包到外部的服務提供者可幫助僅具備有限運算資源的資料所有人。因為資料可能內含敏感的資訊，資料隱私是運算外包中被嚴重關切的問題。除了資料本身，從資料所產生的分類器亦是資料所有人的私有資產。現有的支持向量機器的隱私保存技巧在安全性上較弱。在第二章中，我們提出了高安全性的具備隱私保存的支持向量機器外包方法。在所提出的方法中，資料經由隨機線性轉換所打亂，因此相較於現有的作品，有較強的安全性。服務提供者從打亂的資料去產生支持向量機器分類器，而且所產生的分類器亦是打亂的形式，服務提供者無法存取。 在第三章，我們探討使用支持向量機器分類器所固有的違反隱私問題。支持向量機器訓練分類器的方法是藉由解決最佳化問題來決定訓練資料集中的那些資料個體做為支持向量。支持向量是提供必要資訊用以組成支持向量機器分類器的資料個體。因為支持向量是從訓練資料集中所取出的完整個體，釋出支持向量機器分類器供公眾或他人使用將會揭露支持向量的私密內容。我們提出一個對支持向量機器分類器作後處理的方法，用以轉換其至具有隱私保存的支持向量機器分類器，來避免揭露支持向量的私密內容。此方法精確的去近似高斯核心函數支持向量機器分類器的決策函數，而不去洩漏個別支持向量的內容。具備隱私保存的支持向量機器分類器可以在不違反個別資料隱私的情況下去釋出支持向量機器分類器的預測能力。 支持向量機器的效率亦是個重要的議題。因為對於大規模的資料，支持向量機器的解法收斂得很慢。在第四章，我們利用在第三章所發展的核心近似技術，用以設計一個高效率的支持向量機器訓練演算法。雖然核心函數給支持向量機器帶來了強大的分類能力，但是在訓練過程亦導致了額外的運算成本。相對的，訓練線性支持向量機器有較快的解法。我們使用核心近似技術由明確的低維度特徵值的內積去計算核心值，以利用高效率的線性支持向量機器解法去訓練非線性支持向量機器。此法不僅是一個高效率的訓練方法，還能直接獲得具備隱私保存的支持向量機器分類器，亦即其分類器沒有揭露任何的資料個體。 我們做了廣泛的實驗用以驗證所提出的方法。實驗結果顯示，具備隱私保存的支持向量機器外包方法、具備隱私保存的支持向量機器分類器，以及基於核心近似的高效率支持向量機器訓練方法，皆可達到相似於一般支持向量機器分類器的分類精確度，並且具備了隱私保存及高效率的特性。Data classification is a widely used data mining technique which learns classifiers from labeled data to predict the labels of unlabeled instances. Among data classification algorithms, the support vector machine (SVM) shows the state-of-the-art performance. Data privacy is a critical concern in applying the data mining techniques. In this dissertation, we study how to achieve privacy-preservation in utilizing the SVM as well as how to efficiently generate the SVM classifier. Outsourcing has become popular in current cloud computing trends. Since the training algorithm of the SVM involves intensive computations, outsourcing to external service providers can benefit the data owner who possesses only limited computing resources. In outsourcing, the data privacy is a critical concern since there may be sensitive information contained in the data. In addition to the data, the classifier generated from the data is also private to the data owner. Existing privacy-preserving SVM outsourcing technique is weak in security. In Chapter 2, we propose a secure privacy-preserving SVM outsourcing scheme. In the proposed scheme, the data are perturbed by random linear transformation which is stronger in security than existing works. The service provider generates the SVM classifier from the perturbed data where the classifier is also in perturbed form and cannot be accessed by the service provider. In Chapter 3, we study the inherent privacy violation problem in the SVM classifier. The SVM trains a classifier by solving an optimization problem to decide which instances of the training dataset are support vectors, which are the necessarily informative instances to form the SVM classifier. Since support vectors are intact tuples taken from the training dataset, releasing the SVM classifier for public use or other parties will disclose the private content of support vectors. We propose an approach to post-process the SVM classifier to transform it to a privacy-preserving SVM classifier which does not disclose the private content of support vectors. It precisely approximates the decision function of the Gaussian kernel SVM classifier without exposing the individual content of support vectors. The privacy-preserving SVM classifier is able to release the prediction ability of the SVM classifier without violating the individual data privacy. The efficiency of the SVM is also an important issue since for large-scale data, the SVM solver converges slowly. In Chapter 4, we design an efficient SVM training algorithm based on the kernel approximation technique developed in Chapter 3. The kernel function brings powerful classification ability to the SVM, but it incurs additional computational cost in the training process. In contrast, there exist faster solvers to train the linear SVM. We capitalize the kernel approximation technique to compute the kernel evaluation by the dot product of explicit low-dimensional features to leverage the efficient linear SVM solver for training a nonlinear kernel SVM. In addition to an efficient training scheme, it obtains a privacy-preserving SVM classifier directly, i.e., its classifier does not disclose any individual instance. We conduct extensive experiments over our studies. Experimental results show that the privacy-preserving SVM outsourcing scheme, the privacy-preserving SVM classifier, and the efficient SVM training scheme based on kernel approximation achieve similar classification accuracy to a normal SVM classifier while obtains the properties of privacy-preservation and efficiency respectively

Facile Synthesis of Carbon- and Nitrogen-Doped Iron Borate as a Highly Efficient Single-Component Heterogeneous Photo-Fenton Catalyst under Simulated Solar Irradiation

The development of a heterogeneous catalyst for use in environmental remediation remains a challenging and attractive research endeavor. Specifically, for Fenton reactions, most research approaches have focused on the preparation of iron-containing heterostructures as photo-Fenton catalysts that utilize visible light for enhancing the degradation efficiency. Herein, the synthesis and novel application of C,N-doped iron borates are demonstrated as single-component heterogeneous photo-Fenton catalysts with high Fenton activity under visible light. Under the optimal conditions, 10 mg of the catalyst is shown to achieve effective degradation of 10 ppm methylene blue (MB) dye, Rhodamine B (RhB) dye, and tetracycline (TC) under simulated solar irradiation with a first-order rate constant of k = 0.218 min−1, 0.177 min−1, and 0.116 min−1, respectively. Using MB as a model system, the C,N-doped iron borate exhibits 10- and 26-fold increases in catalytic activity relative to that of the 50 nm hematite nanoparticles and that of the non-doped iron borate, respectively, in the presence of H2O2 under the simulated solar irradiation. Furthermore, the optimum reaction conditions used only 320 equivalents of H2O2 with respect to the concentration of dye, rather than the several thousand equivalents of H2O2 used in conventional heterogeneous Fenton catalysts. In addition, the as-prepared C,N-doped iron borate achieves 75% MB degradation after 20 min in the dark, thus enabling the continuous degradation of pollutants at night and in areas with poor light exposure. The stability and recyclability of C,N-doped iron borate for the oxidation of MB was demonstrated over three cycles with insignificant loss in photo-Fenton activity. The high Fenton activity of the C,N-doped iron borate is considered to be due to the synergistic action between the negatively-charged borate ligands and the metal center in promoting the Fenton reaction. Moreover, carbon and nitrogen doping are shown to be critical in modifying the electronic structure and increasing the conductivity of the catalyst. In view of its synthetic simplicity, high efficiency, low cost of reagents, and minimal cost of operation (driven by natural sunlight), the as-prepared heterogeneous single-component metal borate catalyst has potential application in the industrial treatment of wastewater