Miracle-dz/UniFAR-FTUs huggingface dataset:
Pre- and/or Intra-Operative Prescription of Diuretics, but Not Renin-Angiotensin-System Inhibitors, Is Significantly Associated with Acute Kidney Injury after Non-Cardiac Surgery: A Retrospective Cohort Study

Background and Objectives Pre- and/or intra-operative use of diuretics, angiotensin-converting enzyme inhibitors (ACE-I) or angiotensin II receptor blockers (ARB) constitutes a potentially modifiable risk factor for postoperative acute kidney injury (AKI). It has been studied whether use of these drugs predicts AKI after cardiac surgery. The objective of this study was to examine whether administration of these agents was independently associated with AKI after non-cardiac surgery. Design, Setting, Participants, and Measurements This was a retrospective observational study. Inclusion criteria were adult patients (age ≥ 18) who underwent non-cardiac surgery under general anesthesia from 2007 to 2009 at Kyoto Katsura Hospital. Exclusion criteria were urological surgery, missing creatinine values, and preoperative dialysis. The exposures of interest were pre- and/or intra-operative use of diuretics or ACE-I/ARB. Outcome variables were postoperative AKI as defined by the AKI Network (increase in creatinine ≥ 0.3 mg/dL or 150% within 48 hours, or urine output < 0.5 ml/kg/hour for > 6 hours). Multivariable logistic regression analyses were conducted and adjusted for potential confounders. Propensity scores (PS) for receiving diuretics or ACE-I/ARB therapy were estimated and PS adjustment, PS matching, and inverse probability weighting were performed. Results There were 137 AKI cases (5.0%) among 2,725 subjects. After statistical adjustment for patient and surgical characteristics, odds (95% CI) of postoperative AKI were 2.07 (1.10-3.89) (p = 0.02) and 0.89 (0.56-1.42) (p = 0.63) in users of diuretics and ACE-I/ARB, respectively, compared with non-users. PS adjustment, PS matching, and inverse probability weighting yielded similar results. The effect size of diuretics was significantly greater in the patients with lower propensity for diuretic use (p for interaction < 0.1). Conclusions Prescription of diuretics, but not ACE-I/ARB, was independently associated with postoperative AKI after non-cardiac surgery, especially in patients with low propensity for diuretic use. It might be reasonable to withhold preoperative diuretics in these patients.
Application of convolutional neural networks to speaker recognition in noisy conditions

This paper applies a convolutional neural network (CNN) trained for automatic speech recognition (ASR) to the task of speaker identification (SID). In the CNN/i-vector front end, the sufficient statistics are collected based on the outputs of the CNN as opposed to the traditional universal background model (UBM). Evaluated on heavily degraded speech data, the CNN/i-vector front end provides performance comparable to the UBM/i-vector baseline. The combination of these approaches, however, is shown to provide improvements of 26% in miss rate to considerably outperform the fusion of two different features in the traditional UBM/i-vectors approach. An analysis of the language- and channel-dependency of the CNN/i-vector approach is also provided to highlight future research directions. Index Terms: Deep neural networks, Convolutional neural networks, Speaker recognition, i-vectors, noisy speech
Classifying streaming data using grammar-based immune programming

This work proposes a technique for classifying unlabelled streaming data using grammar-based immune programming, a hybrid meta-heuristic where the space of grammar generated solutions is searched by an artificial immune system inspired algorithm. Data is labelled using an active learning technique and is buffered until the system trains adequately on the labelled data. The proposed system is tested and evaluated using synthetic and real-world data. The performance of the system is compared with two benchmark problems. The proposed classification system adapted well to the changing nature of streaming data and the active learning technique made the process less computationally expensive by retaining only those instances which favoured the training process.
Risk Management: the Talking Cure

The use of risk management as a response to ‘strategic’ organizational uncertainties is investigated. The deconstruction of uncertainties to rationalized probabilities is argued to be symptomatic of a specific conceptualization of problems as ‘tame’, a narrow epistemology that fails to account fully for organizational experience. By introducing ‘messes’ and ‘wicked problems’, a new mode of rhetorical, allegorical risk management is argued for. Insights from Machiavelli and psychoanalysis provide frameworks by which this can be achieved.
Imagining Experiencing an Event in the Future Inflates Certainty That It Occurred in the Past

Imagination inflation occurs when participants increase their certainty that they have experienced an event after they imagine the event occurring. Two experiments (with a total of 291 participants) examined the effects of imagining events in the future on participants’ certainty they had experienced those events in the past. Participants rated their certainty in having experienced events and then imagined experiencing some of those events either in the future or in the past. One or two weeks later, participants completed certainty ratings a second time and completed some individual difference measures. In both imagination conditions (future and past), certainty ratings increased more for imagined events than for control events. Autobiographical memory specificity and self-concept clarity did not significantly predict this effect. These findings suggest that imagining events in the future makes people more certain that they have happened in the past.
Probability distribution of returns in the Heston model with stochastic volatility

Abstract We study the Heston model, where the stock price dynamics is governed by a geometrical (multiplicative) Brownian motion with stochastic variance. We solve the corresponding Fokker‐Planck equation exactly and, after integrating out the variance, find an analytic formula for the time‐dependent probability distribution of stock price changes (returns). The formula is in excellent agreement with the Dow‐Jones index for time lags from 1 to 250 trading days. For large returns, the distribution is exponential in log‐returns with a time‐dependent exponent, whereas for small returns it is Gaussian. For time lags longer than the relaxation time of variance, the probability distribution can be expressed in a scaling form using a Bessel function. The Dow‐Jones data for 1982–2001 follow the scaling function for seven orders of magnitude.
Altered Pancreatic Growth and Insulin Secretion in WSB/EiJ Mice

These data suggest that insulin secretion in WSB mice is blunted specifically in vivo, either due to a reduced insulin requirement and/or due to factors that are absent or destroyed in vitro. These studies also highlight the role of post-natal growth in determining adult β-cell mass. Mice are important animal models for the study of metabolic physiology and the genetics of complex traits. Wild-derived inbred mouse strains, such as WSB/EiJ (WSB), are unrelated to the commonly studied mouse strains and are valuable tools to identify novel genes that modify disease risk. We have previously shown that in contrast to C57BL/6J (B6) mice, WSB mice fed a high fat diet do not develop hyperinsulinemia or insulin resistance, and had nearly undetectable insulin secretion in response to an intraperitoneal glucose challenge. As hyperinsulinemia may drive obesity and insulin resistance, we examined whether defects in β-cell mass or function could contribute to the low insulin levels in WSB mice. In young WSB mice, β-cell mass was similar to B6 mice. However, we found that adult WSB mice had reduced β-cell mass due to reduced pancreatic weights. Pancreatic sizes were similar between the strains when normalized to body weight, suggesting their pancreatic size is appropriate to their body size in adults, but overall post-natal pancreatic growth was reduced in WSB mice compared to B6 mice. Islet architecture was normal in WSB mice. WSB mice had markedly increased insulin secretion from isolated islets in vitro. These data suggest that insulin secretion in WSB mice is blunted specifically in vivo, either due to a reduced insulin requirement and/or due to factors that are absent or destroyed in vitro. These studies suggest that WSB mice may provide novel insight into mechanisms regulating insulin secretion and also highlight the role of post-natal growth in determining adult β-cell mass.
Understanding the Impact of Chicana Feminism on College Success: A Literature Review

Chicanas and Latinas are still severely underrepresented in higher education. Much of the previous literature focuses on the barriers to their success. This article highlights studies related to Chicana Feminism and the ways this perspective can positively impact college attainment and success. The authors conclude by discussing implications of biculturalism and further recommendations developing biculturalism among Chicanas and Latinas.
PENGARUH KOMITMEN ORGANISASI, MOTIVASI DAN KOMPENSASI PADA KINERJA MANAJERIAL DENGAN INTERNAL LOCUS OF CONTROL SEBAGAI VARIABEL MODERASI

Penelitian ini bertujuan untuk menguji pengaruh komitmen organisasi, motivasi dan kompensasi terhadap kinerja manajerial dengan internal locus of control sebagai variabel moderasi. Obyek dalam penelitian ini adalah FKTP BPJS Kesehatan di Kabupaten Kudus. Dengan menggunakan populasi sebanya 116 FKTP di Kabupaten Kudus menggunakan sampel populasi. Teknik analisis yang digunakan menggunakan Structural Equation Modeling (SEM). Hasil dari penelitian ini menunjukkan bahwa komitmen organisasi, motivasi, dan kompensasi mempunyai pengaruh yang positif signifikan terhadap kinerja manajerial, dan internal locus of control terbukti mampu memperkuat hubungan antara komitmen organisasi, motivasi, dan kompensasi dengan kinerja manajerial pada FKTP BPJS Kesehatan di Kabupaten Kudus.
THE INFLUENCE OF UTAUT FACTORS ON E-RETENTION WITH E-SATISFACTION AS MEDIATING VARIABLE IN E-LEARNING

Factors on Unified Theory of Acceptance and Use of Technology (UTAUT) are factors derived from UTAUT technology acceptance model, which is used to describe the factors that influence the acceptance of individuals on the implementation of a new technology in the enterprise. This study aimed to analyze the influence of UTAUT factors on E-retention with E-satisfaction as mediating variable. This study uses a quantitative approach with the total samples. The results showed that: (1) Performance Expectancy influences E-retention significantly, (2) Effort Expectancy influences E-retention significantly, (3) Social Influence influences E-retention not significantly, (4) Facilitating Conditions influences E-retention significantly, (5) E-satisfaction influences E-retention significantly, and (6) UTAUT Factors influence E-retention with E-satisfaction as mediating variable. However, the bigger impact is shown through the influence of UTAUT Factors on E-retention directly without going through the E-satisfaction.
Nordihydroguaiaretic acid enhances the activities of aminoglycosides against methicillin- sensitive and resistant Staphylococcus aureus in vitro and in vivo

Infections caused by methicillin-sensitive Staphylococcus aureus (MSSA) and methicillin-resistant S. aureus (MRSA) are prevalent. MRSA infections are difficult to treat and there are no new classes of antibiotics produced to the market to treat infections caused by the resistant bacteria. Therefore, using antibiotic enhancers to rescue existing classes of antibiotics is an attractive strategy. Nordihydroguaiaretic acid (NDGA) is an antioxidant compound found in extracts from plant Larrea Tridentata. It exhibits antimicrobial activity and may target bacterial cell membrane. Combination efficacies of NDGA with many classes of antibiotics were examined by chequerboard method against 200 clinical isolates of MRSA and MSSA. NDGA in combination with gentamicin, neomycin, and tobramycin was examined by time-kill assays. The synergistic combinations of NDGA and aminoglycosides were tested in vivo using a murine skin infection model. Calculations of the fractional inhibitory concentration index (FICI) showed that NDGA when combined with gentamicin, neomycin, or tobramycin displayed synergistic activities in more than 97% of MSSA and MRSA, respectively. Time kill analysis demonstrated that NDGA significantly augmented the activities of these aminoglycosides against MRSA and MSSA in vitro and in murine skin infection model. The enhanced activity of NDGA resides on its ability to damage bacterial cell membrane leading to accumulation of the antibiotics inside bacterial cells. We demonstrated that NDGA strongly revived the therapeutic potencies of aminoglycosides in vitro and in vivo. This combinational strategy could contribute major clinical implications to treat antibiotic resistant bacterial infections.
Thermo-mechanical simulation to optimize the integration of a BST stacked MIMIM capacitor

For the last decade, paraelectric BaxSr1-xTiO3 (BST) thin films have been especially studied to fabricate MIM capacitor for capacitance tuning applications. This paper describes the mechanisms of cracks apparition under BST stacked MIMIM capacitors (Metal Insulator Metal Insulator Metal) built on silicon substrate. The methodology used in this study to have a further understanding of this phenomenon is to investigate 2D process simulations, based on an elastic model. Hence, it could be evidenced that the gap between the extreme stress levels induced by an annealing performed at the end of the capacitor manufacturing is the main contributor in the crack formation. Then, the change from silicon to a sapphire substrate was implemented to avoid cracks in the real process integration. Finally, the capacitor devices could be tested and were demonstrated to exhibit better electrical specifications.
Learning Rates of lq Coefficient Regularization Learning with Gaussian Kernel

Regularization is a well-recognized powerful strategy to improve the performance of a learning machine and lq regularization schemes with are central in use. It is known that different q leads to different properties of the deduced estimators, say, l2 regularization leads to a smooth estimator, while l1 regularization leads to a sparse estimator. Then how the generalization capability of lq regularization learning varies with q is worthy of investigation. In this letter, we study this problem in the framework of statistical learning theory. Our main results show that implementing lq coefficient regularization schemes in the sample-dependent hypothesis space associated with a gaussian kernel can attain the same almost optimal learning rates for all . That is, the upper and lower bounds of learning rates for lq regularization learning are asymptotically identical for all . Our finding tentatively reveals that in some modeling contexts, the choice of q might not have a strong impact on the generalization capability. From this perspective, q can be arbitrarily specified, or specified merely by other nongeneralization criteria like smoothness, computational complexity or sparsity.
An Attribute-Based Dynamic Data Organization in Mass Storage Systems

With the growing scale of storage systems, the single and static data organization strategies cannot satisfy the increasing demand of high performance and reliable I/O. One solution is to adjust data organization manually by administrators periodically, which causes high labor cost. This paper presents a dynamic data organization in mass storage systems based on an attribute-managed approach. The proposed attribute-managed approach describes and classifies the workload attributes in storage systems, and associating the workload attributes with certain data organization strategies. Storage objects are placed with the most appropriate data organization according to attributes pertaining to them. To demonstrate the effectiveness of our proposed solution, we developed a prototype of attribute-managed storage system by extending the existing OSD and iSCSI protocols. The measurement results show that the proposed attribute-based approach achieves high performance.
Implementation of an Efficient Web Defacement Detection Technique And Spotting Exact Defacement L ocation Using Diff Algorithm

— When a hacker changes the page of a website to something other than what was originally there, then it is called a web defacement. Website defacement is an attack on a website that changes the visual appearance of the site or a webpage [1]. These are typically the work of system crackers, who break into a web server and replace the hosted website with one of their own. The most common method of defacement is using SQL Injections to log on to administrator accounts. Defacements usually consist of an entire page. This page usually includes the defacer's pseudonym or "Hacking Codename. " Sometimes, the Website Defacer makes fun of the system administrator for failing to maintain server security [5]. Most times, the defacement is harmless, however, it can sometimes be used as a distraction to cover up more sinister actions such as uploading malware or deleting essential files from the server. Web defacement results in extreme embarrassment to the web site owner, regardless of the commercial interest in the web site [7]. However, persons and companies who are targets of web defacement, often have substantial interest in maintaining the professional image and integrity of the web site. This paper proposes a hash code based web defacement detection mechanism [1]. We also propose an on spot defacement detection methodology using diff algorithm which can be used to recover the original web
QED effective action in time dependent electric backgrounds

We apply the resolvent technique to the computation of the QED effective action in time dependent electric field backgrounds. The effective action has both real and imaginary parts, and the imaginary part is related to the pair production probability in such a background. The resolvent technique has been applied previously to spatially inhomogeneous magnetic backgrounds, for which the effective action is real. We explain how dispersion relations connect these two cases, the magnetic case which is essentially perturbative in nature, and the electric case where the imaginary part is nonperturbative. Finally, we use a uniform semiclassical approximation to find an expression for very general time dependence for the background field. This expression is remarkably similar in form to Schwinger{close_quote}s classic result for the constant electric background. {copyright} {ital 1998} {ital The American Physical Society}
Hypermethylation of multiple genes in pancreatic adenocarcinoma.

Hypermethylation of CpG islands is a common mechanism by which tumor suppressor genes are inactivated. We studied 45 pancreatic carcinomas and 14 normal pancreata for aberrant DNA methylation of CpG islands of multiple genes and clones using methylation-specific PCR (MSP) and bisulfite-modified sequencing. Using MSP, we detected aberrant methylation of at least one locus in 60% of carcinomas. The genes analyzed included RARbeta (methylated in 20%), p16 (18%), CACNA1G (16%), TIMP-3 (11%), E-cad (7%), THBS1 (7%), hMLH1 (4%), DAP kinase (2%), and MGMT (0%). In addition, aberrant methylation was found in three CpG islands (MINT31, -1, and -2) in 38, 38, and 14% of carcinomas, respectively. Hypermethylation was largely confined to the carcinomas with only three loci (E-cad, DAP kinase, and MINT2) harboring methylation in some normal pancreata (36, 21, and 14%, respectively). Simultaneous methylation of at least four loci was observed in 5 of 36 (14%) pancreatic adenocarcinomas. We defined this subgroup of pancreatic adenocarcinomas as "CpG island-methylator-phenotype positive (CIMP+). " Two of four carcinomas with microsatellite instability harbored promoter hypermethylation of hMLH1, and both cases were CIMP+. Thus, we conclude that many pancreatic carcinomas hypermethylate a small percentage of genes, whereas a subset displays a CIMP+ phenotype.
Intra-cluster training strategy for deep learning with applications to language identification

In this study we address the problem of training a neural network for language identification using speech samples in the form of i-vectors. Our approach involves training a classifier and analyzing the obtained confusion matrix. We cluster the languages by simultaneously clustering the columns and the rows of the confusion matrix. The language clusters are then used to define a modified cost function for training a neural-network that focuses on distinguishing between the true language and languages within the same cluster. The results show enhanced language identification on the NIST 2015 language identification dataset.
An approach for elderly daily behavior habits extraction for service robot using position information

This paper proposes an approach by using the position information in home environment to learn human daily behavior habit from sensor data. Firstly, we build a layered framework which is suitable for service robot and can be applied in home environment to monitor the behavior states of elderly human. Then, we apply the improved basic sequential algorithmic scheme and Fuzzy C-Means Clustering to extract the disciplinarian of human daily living. The emulational experiment results demonstrate that our approach is fit for the problem we address.
A fall detection method based on acceleration data and hidden Markov model

Falls have been a major health risk that diminishes the quality of life among the elderly. In this paper, we propose a new method using acceleration data and hidden Markov model (HMM) to detect fall events. A wearable device integrating a tri-axial accelerometer was used to collect acceleration data of human chest. Feature sequences (FSs) were extracted from the acceleration data and used as sequence of observations to train an HMM of fall detection. The probability of the input FS generated by the model was calculated as the detection standard. Experimental results showed that the accuracy of the proposed method was 97.2%, the sensitivity was 91.7%, and the specificity was 100%, demonstrating desired performance of our method in detecting fall events.
Steering vector uncertainties and diagonal loading

In this paper, we study the performance of diagonally loaded beam-formers in the presence of steering vector errors i.e. when there exists a mismatch between the actual steering vector of interest and the presumed one. We consider the problem of optimally selecting the loading level with a view to maximise the signal to interference plus noise ratio. Ignoring finite-sample effects we show that, in the presence of strong low-rank interference and broadband noise, the optimal loading level is negative. Numerical simulations attest to the validity of the analysis and show that diagonal loading with the optimal loading factor derived herein provides a performance close to optimum. It is also shown that negative diagonal loading is effective in finite-sample as long as the steering vector errors dominate over the finite-sample errors.