Miracle-dz/UniFAR-FTUs huggingface dataset:
CPPI Method with a Conditional Floor

We propose an extension of the CPPI method, which is based on conditional floors. In this framework, we examine in particular the margin based strategies. This method allows to keep part of the past gains and to protect the portfolio value against future high drawdowns of the financial market. However, as for the standard CPPI method, the investor can benefit from potential market rises. To control the risk of such strategies, we introduce the Value-at-Risk (VaR) as risk measure. We show that the conditional floor must be higher than a lower bound. We illustrate these results, for a quite general ARCH type model, including the Egarch(1,1) as a special case.
Human papillomavirus in upper digestive tract tumors from three countries.

AIM To clarify human papillomavirus (HPV) involvement in carcinogenesis of the upper digestive tract of virological and pathological analyses. METHODS The present study examined the presence of HPV in squamous cell carcinomas of the oral cavity (n = 71), and esophagus (n = 166) collected from Japan, Pakistan and Colombia, with different HPV exposure risk and genetic backgrounds. The viral load and physical status of HPV16 and HPV16-E6 variants were examined. Comparison of p53 and p16(INK4a) expression in HPV-positive and HPV-negative cases was also made. RESULTS HPV16 was found in 39 (55%) oral carcinomas (OCs) and 24 (14%) esophageal carcinomas (ECs). This site-specific difference in HPV detection between OCs and ECs was statistically significant (P < 0.001). There was a significant difference in the geographical distribution of HPV16-E6 variants. Multiple infections of different HPV types were found in 13 ECs, but multiple infections were not found in OCs. This difference was statistically significant (P = 0.001). The geometric means (95% confidence interval) of HPV16 viral load in OCs and ECs were 0.06 (0.02-0.18) and 0.12 (0.05-0.27) copies per cell, respectively. The expression of p16(INK4a) proteins was increased by the presence of HPV in ECs (53% and 33% in HPV-positive and -negative ECs, respectively; P = 0.036), and the high-risk type of the HPV genome was not detected in surrounding normal esophageal mucosa of HPV-positive ECs. CONCLUSION Based on our results, we cannot deny the possibility of HPV16 involvement in the carcinogenesis of the esophagus.
An automatic text comprehension classifier based on mental models and latent semantic features

Reading comprehension is one of the main concerns for educational institutions, as it forges the students' ability to comprehend and learn accurately a given information source (e.g. textbooks, articles, papers, etc.). However, there are few approaches that integrates digital sources of educational information with automated systems to detect whether an individual has comprehended a given reading task. This work main contribution is a text comprehension classification methodology for the detection of reading comprehension failures in educational institutions. The proposed approach relates situational model theories and latent semantic analysis from fields of psycholinguistics and natural language processing respectively. A numerical characterization of students' documents using structural information, such as the usage of text connectors, and latent semantic features are used as input for traditional classification algorithms. Therefore, an automated classifier is built to determine whether a given student could or not comprehend the information in the given stimulus documents. For the evaluation of the proposed methodology, using a set of stimulus documents, a set of questions must be answered by an experimental group of students. We have performed experiments using first year students from Engineering and Linguistics undergraduate schools at the University of Chile with promising results.
Structural Testing of Concurrent Programs

Although structural testing techniques are among the weakest available with regard to developing confidence in sequential programs, they are not without merit. The authors extend the notion of structural testing criteria to concurrent programs and propose a hierarchy of supporting structural testing techniques. Coverage criteria described include concurrency state coverage, state transition coverage and synchronization coverage. Requisite support tools include a static concurrency analyzer and either a program transformation system or a powerful run-time monitor. Also helpful is a controllable run-time scheduler. The techniques proposed are suitable for Ada or CSP-like languages. Best results are obtained for programs having only static naming of tasking objects. >
Properties of Hyperpolarization-Activated Pacemaker Current Defined by Coassembly of Hcn1 and Hcn2 Subunits and Basal Modulation by Cyclic Nucleotide

Members of the HCN channel family generate hyperpolarization-activated cation currents (Ih) that are directly regulated by cAMP and contribute to pacemaker activity in heart and brain. The four HCN isoforms show distinct but overlapping patterns of expression in different tissues. Here, we report that HCN1 and HCN2, isoforms coexpressed in neocortex and hippocampus that differ markedly in their biophysical properties, coassemble to generate heteromultimeric channels with novel properties. When expressed in Xenopus oocytes, HCN1 channels activate 5–10-fold more rapidly than HCN2 channels. HCN1 channels also activate at voltages that are 10–20 mV more positive than those required to activate HCN2. In cell-free patches, the steady-state activation curve of HCN1 channels shows a minimal shift in response to cAMP (+4 mV), whereas that of HCN2 channels shows a pronounced shift (+17 mV). Coexpression of HCN1 and HCN2 yields Ih currents that activate with kinetics and a voltage dependence that tend to be intermediate between those of HCN1 and HCN2 homomers, although the coexpressed channels do show a relatively large shift by cAMP (+14 mV). Neither the kinetics, steady-state voltage dependence, nor cAMP dose–response curve for the coexpressed Ih can be reproduced by the linear sum of independent populations of HCN1 and HCN2 homomers. These results are most simply explained by the formation of heteromeric channels with novel properties. The properties of these heteromeric channels closely resemble the properties of Ih in hippocampal CA1 pyramidal neurons, cells that coexpress HCN1 and HCN2. Finally, differences in Ih channel properties recorded in cell-free patches versus intact oocytes are shown to be due, in part, to modulation of Ih by basal levels of cAMP in intact cells.
Some Sampling Properties of Common Phase Estimators

Abstract The instantaneous phase of neural rhythms is important to many neuroscience-related studies. In this letter, we show that the statistical sampling properties of three instantaneous phase estimators commonly employed to analyze neuroscience data share common features, allowing an analytical investigation into their behavior. These three phase estimators—the Hilbert, complex Morlet, and discrete Fourier transform—are each shown to maximize the likelihood of the data, assuming the observation of different neural signals. This connection, explored with the use of a geometric argument, is used to describe the bias and variance properties of each of the phase estimators, their temporal dependence, and the effect of model misspecification. This analysis suggests how prior knowledge about a rhythmic signal can be used to improve the accuracy of phase estimates.
Acoustic-Linguistic Recognition of Interest in Speech with Bottleneck-BLSTM Nets

This paper proposes a novel technique for speech-based interest recognition in natural conversations. We introduce a fully automatic system that exploits the principle of bidirectional Long Short-Term Memory (BLSTM) as well as the structure of so-called bottleneck networks. BLSTM nets are able to model a self-learned amount of context information, which was shown to be beneﬁcial for affect recognition applications, while bottleneck networks allow for efﬁcient feature compression within neural networks. In addition to acoustic features, our technique considers linguistic information obtained from a multi-stream BLSTM-HMM speech recognizer. Evaluations on the TUM AVIC corpus reveal that the bottleneck-BLSTM method pre-vails over all approaches that have been proposed for the Inter-speech 2010 Paralinguistic Challenge task.
3D Face Recognition

Biometric systems for human recognition are an ongoing demand. Among all biometric technologies which are employed so far, face recognition is one of the most widely outspread biometrics. Its daily use by nearly everyone as the primary mean for recognizing other humans and its naturalness have turned face recognition into a well-accepted method. Furthermore, this image procurement is not considered as intrusive as the other mentioned alternatives. Nonetheless, in spite of the various facial recognition systems which already exist, many of them have been unsuccessful in matching up to expectations. 2D facial recognition systems are constrained by limitations such as physical appearance changes, aging factor, pose and changes in lighting intensity. Recently, to overcome these challenges 3D facial recognition systems have been issued as the newly emerged biometric technique, showing a high level of accuracy and reliability, being more robust to face variation due to the different factors. A face-based biometric system consists of acquisition devices, preprocessing, feature extraction, data storage and a comparator. An acquisition device may be a 2D-, 3Dor an infra-redcamera that can record the facial information. The preprocessing can detect facial landmarks, align facial data and crop facial area. It can filter irrelevant information such as hair, background and reduce facial variation due to pose change. In 2D images, landmarks such as eye, eyebrow, mouths etc, can be reliably detected, in contrast, nose is the most important landmark in 3D face recognition. The 3D information (depth and texture maps) corresponding to the surface of the face may be acquired using different alternatives: A multi camera system (stereoscopy), range cameras or 3D laser and scanner devices. Different approaches have been presented from the 3D perspective. The first approach would correspond to all 3D approaches that require the same data format in the training and in the testing stage. The second philosophy would enclose all approaches that take advantage of the 3D data during the training stage but then use 2D data in the recognition stage. Approaches of the first category report better results than of the second group; however, the main drawback of this category is that the acquisition conditions and elements of the test scenario should be well synchronized and controlled in order to acquire accurate 3D data. Thus, they are not suitable for surveillance applications or control access points where only one “normal” 2D texture image (from any view) acquired from a single camera is available. The second category encloses model-based approaches. Nevertheless, model-based face recognition approaches present the main drawback of a high computational burden required to fit the images to the 3D models.
Expression of Lipid Metabolism-Related Proteins in Metastatic Breast Cancer

Purpose The tumor biology of metastatic breast cancers differ according to the metastatic sites, and the features of cancer metabolism may also be different. The aim of this study is to investigate the expression of lipid metabolism-related proteins in metastatic breast cancer according to metastatic site and discuss the clinical significance thereof. Methods Immunohistochemical staining for lipid metabolism-related proteins [fatty acid synthase (FASN), hormone-sensitive lipase (HSL), carnitine palmitoyltransferase IA (CPT-1A), acyl-CoA oxidase 1 (ACOX1), fatty acid binding protein 4 (FABP4,) and perilipin 1 (PLIN1)] was performed using a tissue microarray of 149 cases of metastatic breast cancer (bone metastasis = 39, brain metastasis = 37, liver metastasis = 21, and lung metastasis = 52). Results The expression levels of ACOX1 (p = 0.009) and FASN (p = 0.007) varied significantly according to metastatic site, with the highest expression in brain metastasis and the lowest expression in liver metastasis. ACOX1 positivity (p = 0.005) and FASN positivity (p = 0.003) correlated with HER-2 positivity. The expression of FASN was significantly higher in HER-2 type breast cancer, and lower in luminal A and TNBC type breast cancer (p<0.001). Among lipid metabolism-related proteins, PLIN1 positivity was found to be an independent poor prognostic factor on multivariate analysis (Hazard ratio: 4.979, 95% CI: 1.054–22.59, p = 0.043). Conclusion Different expression levels of lipid metabolism-related proteins were observed according to metastatic site. The expression of ACOX1 and FASN was highest in brain metastasis. These results suggest that the metastatic site should be considered when using lipid metabolism inhibitors for targeted therapy.
Two-step subspace learning for texture synthesis of facial images

In recent years, animation of dynamic facial expression got many attentions in entertainment and other fields. In this paper, we describe a new useful method for synthesis of facial images (ex. different expression, different view point) from one natural facial image. A lot of methods such as subspace learning have been proposed for synthesis of facial images. But the synthesis accuracy by existing methods is not enough, especially for texture synthesis. In this paper, we propose a two-step subspace learning method to improve the synthesis accuracy. In our proposed method, we add a residual error subspace learning step for reduction of synthesis error. The proposed method has been applied to synthesize expressional facial images.
Smilansky's model of irreversible quantum graphs: I. The absolutely continuous spectrum

In the model suggested by Smilansky (2004 Waves Random Media 14 143–53) one studies an operator describing the interaction between a quantum graph and a system of K one-dimensional oscillators attached at several different points in the graph. The present paper is the first one in which the case K > 1 is investigated. For the sake of simplicity, we consider K = 2, but our argument is of a general character. In this first of two papers on the problem, we describe the absolutely continuous spectrum. Our approach is based upon scattering theory.
Nanoparticle as a novel tool in hyperthermic intraperitoneal and pressurized intraperitoneal aerosol chemotheprapy to treat patients with peritoneal carcinomatosis

The treatment of peritoneal surface malignances has changed considerably over the last thirty years. Unfortunately, the palliative is the only current treatment for peritoneal carcinomatosis (PC). Two primary intraperitoneal chemotherapeutic methods are used. The first is combination of cytoreductive surgery (CRS) and Hyperthermic IntraPEritoneal Chemotherapy (HIPEC), which has become the gold standard for many cases of PC. The second is Pressurized IntraPeritoneal Aerosol Chemotheprapy (PIPAC), which is promising direction to minimally invasive as safedrug delivery. These methods were improved through multicenter studies and clinical trials that yield important insights and solutions. Major method development has been made through nanomedicine, specifically nanoparticles. Here, we are presenting the latest advances of nanoparticles and their application to precision diagnostics and improved treatment strategies for PC. These advances will likely develop both HIPEC and PIPAC methods that used for in vitro and in vivo studies. Several benefits of using nanoparticles will be discussed including: 1) Nanoparticles as drug delivery systems; 2) Nanoparticles and Near Infrred (NIR) Irradiation; 3) use of nanoparticles in perioperative diagnostic and individualized treatment planning; 4) use of nanoparticles as anticancer dressing’s, hydrogels and as active beeds for optimal reccurence prevention; and 5) finally the curent in vitro and in vivo studies and clinical trials of nanoparticles. The current review highlighted use of nanoparticles as novel tools in improving drug delivery to be effective for treatment patients with peritoneal carcinomatosis.
The class imbalance problem in learning classifier systems: a preliminary study

The class imbalance problem has been said recently to hinder the performance of learning systems. In fact, many of them are designed with the assumption of well-balance datasets. However, it is very common to find higher presence of one of the classes in real classification problems. The aim of this paper is to make a preliminary analysis on the effect of the class imbalance problem in learning classifier systems. Particularly we focus our study on UCS, a supervised version of XCS classifier system. We analyze UCS's behavior on unbalanced datasets and find that UCS is sensitive to high levels of class imbalance. We study strategies for dealing with class imbalances, acting either at the sampling level or at the classifier system's level.
Effects of Different Pediatric Drugs on the Color Stability of Various Restorative Materials Applicable in Pediatric Dentistry

Background. The chronic recommendation of pediatric drugs could exhibit erosive and cariogenic problems. Objective. To evaluate the effects of different pediatric drugs on the color stability of various restorative materials. Methods. Five specimens (1 mm × 3 mm) were prepared and immersed in ten different pediatric drugs and agitated every 8 hours daily for 2 min up to 1 week. Between immersion periods, the samples were stored in artificial saliva. After 1-week period, ΔE⁎ values were calculated. Two-way ANOVA and Fisher's LSD test were used for statistical analysis at a level of p < 0.05. Results. ΔE⁎ values were only significantly influenced by restorative material factor (p < 0.001) and varied in the range of 2.08 and 6.55 units for all drugs/restorative materials. The highest ΔE⁎ was found in Ferrosanol B-composite (6.55 ± 1.38) and the lowest one was found in Dolven-glass ionomer (2.08 ± 0.40) pairwise. The most prominent ΔE⁎ value elevations were obtained in composite material compared to the compomer and/or glass ionomers in Macrol, Ferrosanol B, and Ventolin (p < 0.001; for all) and also for other drugs (p < 0.05). Dolven exhibited significantly higher values compared to Augmentin (p = 0.021), Macrol (p = 0.018), and Ventolin (p = 0.013) in compomer group. Conclusion. The clinically perceptible color changes for tested composite/pediatric drug pairwise can be more problematic than compomer and glass ionomers in pediatric dentistry.
The CO-Regulation Database (CORD): A Tool to Identify Coordinately Expressed Genes

Background Meta-analysis of gene expression array databases has the potential to reveal information about gene function. The identification of gene-gene interactions may be inferred from gene expression information but such meta-analysis is often limited to a single microarray platform. To address this limitation, we developed a gene-centered approach to analyze differential expression across thousands of gene expression experiments and created the CO-Regulation Database (CORD) to determine which genes are correlated with a queried gene. Results Using the GEO and ArrayExpress database, we analyzed over 120,000 group by group experiments from gene microarrays to determine the correlating genes for over 30,000 different genes or hypothesized genes. CORD output data is presented for sample queries with focus on genes with well-known interaction networks including p16 (CDKN2A), vimentin (VIM), MyoD (MYOD1). CDKN2A, VIM, and MYOD1 all displayed gene correlations consistent with known interacting genes. Conclusions We developed a facile, web-enabled program to determine gene-gene correlations across different gene expression microarray platforms. Using well-characterized genes, we illustrate how CORD's identification of co-expressed genes contributes to a better understanding a gene's potential function. The website is found at http://cord-db.org.
Regularized Channel Inversion for Simultaneous Confidential Broadcasting and Power Transfer: A Large System Analysis

We propose for the first time new transmission schemes based on linear precoding to enable simultaneous confidential broadcasting and power transfer in a multiuser multi-input single-output network, where a base station with N antennas simultaneously transmits power and confidential messages to K single-antenna users. We first design two transmission schemes based on the rules of regularized channel inversion (RCI) for both power splitting (PS) and time switching (TS) receiver architectures, namely, RCI-PS and RCI-TS schemes. For each scheme, we derive channel-independent expressions to approximate the secrecy sum rate and the harvested power in the large-system regime where K,N → ∞ with a fixed ratio β =K/N. Based on the large-system results, we jointly optimize the regularization parameter of the RCI and the PS ratio or the TS ratio such that the secrecy sum rate is maximized subject to an energy-harvesting constraint. We then present the tradeoff between the secrecy sum rate and the harvested power achieved by each scheme, and find that neither scheme always outperforms the other one. Motivated by this fact, we design an RCI-hybrid scheme based on the RCI and a newly proposed hybrid receiver architecture. The hybrid receiver architecture takes advantages of both the PS and TS receiver architectures. We show that the RCI-hybrid scheme outperforms both the RCI-PS and RCI-TS schemes.
Manifold based kernel optimization for KPCA

This paper presents a manifold based kernel optimizing algorithm for KPCA which has recently shown effectiveness for pattern recognition and systematic classification based on extracting nonlinear features. However, their performances largely depend on the kernel function. Current methods simply choose the kernel function empirically or experimentally from a given set of candidates. We use manifold learning to improve the kernel function, which is capable to discover the nonlinear degrees of freedom that underlie complex natural observations. In contrast to previous algorithms for kernel optimization, ours efficiently computes a globally optimal solution that is guaranteed to converge asymptotically to the true structure and extracts the nonlinear features better. Experiments show that the method performed well in the field of pattern recognition.
XML-based supply chain management-as SIMPLEX as it is

In this paper we want to examine, to what extent XML is able to support the exchange of business documents in supply chains. Thereby we focus on the problem of converting different data formats of participants of the supply chain. First results show that XML and its surrounding standards of the XML family highly accelerate and simplify the conversion process. Therefore, XML allows using a common standard on a lower level, without reducing variety on a higher level, due to the use of different XML vocabularies. First, we examine different approaches for solving the transformation problem. Second, we show how XML can actually be implemented for a Web-based integration in supply chains. We present a Java-based prototype that enables document exchange over the Internet using XML business vocabularies for document representation, XSLT for document conversion and presentation, and both DOM and SAX for processing and integrating documents into in-house-systems.