Miracle-dz/UniFAR-FTUs huggingface dataset:
Hydroxytyrosol and Potential Uses in Cardiovascular Diseases, Cancer, and AIDS

Hydroxytyrosol is one of the main phenolic components of olive oil. It is present in the fruit and leaf of the olive (Olea europaea L.). During the past decades, it has been well documented that this phenolic compound has health benefits and a protective action has been found in preclinical studies against several diseases. Here, we review its bioavailability in human beings and several assays showing significant results related with cardiovascular diseases, cancer, and acquired immunodeficiency syndrome (AIDS). Mechanisms of action include potent anti-oxidant and anti-inflammatory effects, among others. The importance of hydroxytyrosol in protection of low-density lipoproteins and consequently its implication in the reduction of cardiovascular disease risk has been highlighted by the European Food Safety Authority, concluding that 5 mg of hydroxytyrosol and its derivatives should be consumed daily to reach this effect at physiological level. We discuss the potential uses of this compound in supplements, nutraceutic foods, or topical formulations in the disease risk reduction. Finally, we conclude that more studies are needed to sustain or reject many other health claims not yet fully documented and to validate these newly available hydroxytyrosol-based products, because it seems to be a good candidate to reduce the risk of diseases mentioned.
HC-Search: A Learning Framework for Search-based Structured Prediction

Structured prediction is the problem of learning a function that maps structured inputs to structured outputs. Prototypical examples of structured prediction include part-of-speech tagging and semantic segmentation of images. Inspired by the recent successes of search-based structured prediction, we introduce a new framework for structured prediction called HC-Search. Given a structured input, the framework uses a search procedure guided by a learned heuristic H to uncover high quality candidate outputs and then employs a separate learned cost function C to select a final prediction among those outputs. The overall loss of this prediction architecture decomposes into the loss due to H not leading to high quality outputs, and the loss due to C not selecting the best among the generated outputs. Guided by this decomposition, we minimize the overall loss in a greedy stagewise manner by first training H to quickly uncover high quality outputs via imitation learning, and then training C to correctly rank the outputs generated via H according to their true losses. Importantly, this training procedure is sensitive to the particular loss function of interest and the time-bound allowed for predictions. Experiments on several benchmark domains show that our approach significantly outperforms several state-of-the-art methods.
Completeness classes in algebra

In the theory of recursive functions and computational complexity it has been demonstrated repeatedly that the natural problems tend to cluster together in “completeness classes”. These are families of problems that (A) are computationally interreducible and (B) are the hardest members of some computationally defined class. The aim of this paper is to demonstrate that for both algebraic and combinatorial problems this phenomenon exists in a form that is purely algebraic in both of the respects (A) and (B). Such computational consequences as NP-completeness are particular manifestations of something more fundamental. The core of the paper is self-contained, consisting as it does essentially of the two notions of “p-definability” and the five algebraic relations that are proved as theorems. In the remainder our aim is to elucidate the computational consequences of these basic results. Hence in the auxiliary propositions and discussion for convenience we do assume familiarity with algebraic and Boolean complexity theory.
Increased Constituent Ratios of Klebsiella sp., Acinetobacter sp., and Streptococcus sp. and a Decrease in Microflora Diversity May Be Indicators of Ventilator-Associated Pneumonia: A Prospective Study in the Respiratory Tracts of Neonates

Ventilator-associated pneumonia (VAP) is a common complication and cause of death in neonates on mechanical ventilation. However, it is difficult to define the causes of VAP. To understand the causes of VAP, we undertook a prospective study based on the diversity of the microflora in VAP. The experimental group consisted of newborns who suffered from respiratory distress syndrome (RDS) and VAP, while the control group suffered from RDS without VAP. Sputa were collected within 1, 3, and 5 days of ventilation and were divided into six groups. DNA was extracted from the samples, and the 16S rDNA was PCR amplified, separated using denaturing gradient gel electrophoresis (DGGE), cloned and sequenced. The resulting sequences were compared using BLAST. The DGGE pictures were measured, and the richness, Shannon-Wiener index, and cluster maps were analyzed. No differences were found regarding the constituent ratio of any genus between the Non-VAP and VAP group within 1 day after intubation. After 1 to 3 days, the constituent ratios of Klebsiella sp. , Acinetobacter sp. , and Streptococcus sp. in the VAP group were higher than those in the Non-VAP group, and the ratios of Serratia sp. and Achromobacter sp. were lower. After 3 to 5 days, the ratios of Klebsiella sp. , Acinetobacter sp. , Serratia sp. , and Achromobacter sp. were lower than those in the Non-VAP group. The richness and Shannon-Wiener index of the Non-VAP group were higher than those of the VAP group from 1 to 3 days after intubation, while no differences were found within 1 day and from 3 to 5 days. We conclude that during the first three days of intubation, the microflora diversity in the lower respiratory tract was reduced due to VAP, and the greater constituent ratios of Klebsiella sp. , Acinetobacter sp. , and Streptococcus sp. in the sputum may be indicators of VAP.
Economics of Education

The Economics of Education Program is both exciting and productive, currently adding new Working Papers at the rate of 7.5 per month — a 50 percent increase from the rate at the time of my last program report in fall 2006. The number of papers submitted for a typical Program Meeting is often ten times the number of available slots, and attendance at those meetings is high. I am particularly proud of three aspects of the Program. The first is the quality of the research being produced and the methods used by members, including some of the latest, most rigorous methods in applied microeconometrics. The second is the fact that members use some of the richest, most comprehensive datasets in economics — many of these datasets were initially compiled by schools or school-related organizations, and program members deserve enormous credit for their resourcefulness in making them useful for economic research by establishing strong, collegial relationships with data providers, convincing schools to conduct randomized and other policy experiments, matching data from diverse sources, and themselves surveying or testing people when data otherwise would be missing. Third, program members produce research that is policy relevant, credible to policymakers, and grounded in economic logic. The NBER’s Higher Education Working Group was integrated into the Economics of Education Program in 2009. We made the integration an occasion to celebrate the leadership of Charles T. Clotfelter, director of that working group, who oversaw an immense improvement in the quality of research on the economics of higher education. Although the practical policy questions differ across the two levels of education, all of the methods, much of the data, and much of the deep economic logic are shared.
Showing Where Blind Relevance Feedback Fails

iIn Information Retrieval, relevance feedback has been used to effectively improve retrieval performance. Traditionally this feedback is generated by the user. However, recently there has been some exploration in blind relevance feedback (BRF), an automated means of generating feedback by considering only the top ranked documents as relevant. It has been shown to have a positive effect on retrieval performance. BRF though has no means for dealing with noisy documents that are in the top rank. Thus it should not work well for poor quality queries. Clarity scoring uses relative entropy as a metric for query quality. I believe that this same technique can be used to help determine when BRF will fail.
KronCrypt - A New Symmetric Cryptosystem Based on Kronecker's Approximation Theorem

In this paper we show how to use an old mathematical concept of diophantine analysis, the approximation theorem of Kronecker, in symmetric cryptography. As a first practical application we propose and analyze the new symmetric 128-bit block cipher KronCrypt. The cipher is a 4-round Feistel network with a non-bijective round function f made up of a variable number of large key-dependent S-boxes, XORs and modular additions. Its key length is variable but not less than 128 bit. The main innovation of KronCrypt in the area of symmetric cryptography is the fact that the key-dependent S-boxes are based upon a constructive proof of the approximation theorem of Kronecker used as a boolean function. We prove the correctness of our concept in general and show how we designe the new cipher KronCrypt. Furthermore, results concerning statistical behaviour, i.e. confusion, diffusion and completeness, and differential cryptanalysis are presented.
On the use of memory and resources in minority games

The use of resources in multiagent learning systems is a relevant research problem, with a number of applications in resource allocation, communication and synchronization. Multiagent distributed resource allocation requires that agents act on limited, localized information with minimum communication overhead in order to optimize the distribution of available resources. When requirements and constraints are dynamic, learning agents may be needed to allow for adaptation. One way of accomplishing learning is to observe past outcomes, using such information to improve future decisions. When limits in agents' memory or observation capabilities are assumed, one must decide on how large should the observation window be. We investigate how this decision influences both agents' and system's performance in the context of a special class of distributed resource allocation problems, namely dispersion games. We show by using several numerical experiments over a specific dispersion game (the Minority Game) that in such scenario an agent's performance is non-monotonically correlated with her memory size when all other agents are kept unchanged. We then provide an information-theoretic explanation for the observed behaviors, showing that a downward causation effect takes place.
Physics at LHC

The prospects for physics at the LHC are discussed, starting with the foretaste, preparation (and perhaps scoop) provided by the Tevatron, in particular, and then continuing through the successive phases of LHC operation. These include the start-up phase, the early physics runs, the possible search for new physics in double diffraction, the continuation to nominal LHC running, and the possible upgrade of the LHC luminosity. Emphasis is placed on the prospects for Higgs physics and the search for supersymmetry. The progress and discoveries of the LHC will set the time-scale and agenda for the major future accelerator projects that will follow it.
Temperature of the Great Toe as an Indication of the Severity of Shock

To establish whether changes in skin temperature would provide objective indication of the presence and severity of shock, hemodynamic and temperature measurements were obtained with the aid of a digital computer. Values obtained 3 hours after admission and 3 hours prior to discharge or death in 100 patients who presented with clinical signs of circulatory shock were analyzed. Temperature was measured with standard thermistor probes at five sites: the digital pad of the third finger, the large toe, the deltoid region of the arm, the lateral portion of the thigh, and the rectum.A significant correlation was demonstrated between the cardiac output and temperature of the toe (r = 0.71). Correlations were increased to 0.73 when corrections were made for changes in ambient temperature. A stepwise regression analysis provided no significant improvement in the predictability of cardiac index when the values for temperatures of other skin sites were included. Discriminant function analysis showed that an early measurement of toe temperature correctly predicts patient outcome 67% of the time.
Wave Transmission through Random Layering with Pressure Release Boundary Conditions

This paper considers the statistical properties of the waves generated by a point source in the subsurface and transmitted towards the surface through a randomly layered medium. The problem is analyzed in a regime of separation of scales and with pressure release boundary conditions at the surface. Using a probabilistic representation of the spectral density of the waves received at the surface, the transmitted intensity is analyzed. Conserved quantities specific to the pressure release boundary conditions are evaluated and the power delay spread is computed. In particular a waveguiding effect that is produced by the reflections from the surface and from the random medium is identified and analyzed. This effect is different from the propagation in a standard waveguide from the point of view of the decay of the intensity.
Learning to Detect Negation with ‘Not’ in Medical Texts

While state of the art techniques can address the problem of automatically detecting negated medical observations, negation using the word ‘not’ presents a harder problem than other kinds of negation. We apply machine learning techniques to distinguish sentences where the word ‘not’ does and does not negate a medical observation. Our corpus contains hospital reports such as progress notes and emergency room notes. We use two different machine learning algorithms, Naive Bayes and Decision Trees, and both achieve signiﬁcant improvement over the baseline. We also analyze the data and the classiﬁers’ behavior and output to learn more about the problem and the usefulness of various features in our feature vector
Perceived Self-Efficacy and Financial Incentives: Factors Affecting Health Behavior and Body Weight in Individuals with Overweight and Obesity

Statement of the problem: Overweight (OW) and obesity (OB) is a major public health concern and contributes to escalating health care costs. Self-efficacy plays a significant role in health behavior (both exercise and diet) and lack of could contribute to OW and OB. Design: Exploratory and correlational. Sample size and composition: Ninety-nine middle aged OW and OB female nursing home employees of black and white ethnicities, with at least a high school diploma. Measures: Eating and exercise self-efficacy (Eat-SE, Ex-SE) were assessed using confidencebased questions and then transformed into qualitative scores for each efficacy scale. Healthy eating scores (HES) and exercise behavior were assessed using frequency-based questions. Analysis: Correlation, causal mediation, and regression analysis examined the influence of Eat-SE and ExSE on physical activity (PA), eating behavior, and BMI. Results: Higher HES predicted higher Eat-SE (p= 0.02) and in turn, a lower BMI (p= 0.02). Increased frequencies of moderate and vigorous PA predicted higher Ex-Se (p= 0.01, p= 0.00). Moderate PA further predicted a lower BMI (p= 0.05). Conclusion: Our models combining social cognitive variables (self-efficacy beliefs) and behavioral variables captured variations in BMI. Future obesity intervention may incorporate self-efficacy for overcoming barriers to weight management.
Quantification of peripapillary sparing and macular involvement in Stargardt disease (STGD1).

PURPOSE To quantify and compare structure and function across the macula and peripapillary area in Stargardt disease (STGD1). METHODS Twenty-seven patients (27 eyes) and 12 age-similar controls (12 eyes) were studied. Patients were classified on the basis of full-field electroretinogram (ERG) results: Fundus autofluorescence (FAF) and spectral domain-optical coherence tomography (SD-OCT) horizontal line scans were obtained through the fovea and peripapillary area. The thicknesses of the outer nuclear layer plus outer plexiform layer (ONL+), outer segment (OS), and retinal pigment epithelium (RPE) were measured through the fovea, and peripapillary areas from 1° to 4° temporal to the optic disc edge using a computer-aided, manual segmentation technique. Visual sensitivities in the central 10° were assessed using microperimetry and related to retinal layer thicknesses. RESULTS Compared to the central macula, the differences between controls and patients in ONL+, OS, and RPE layer thicknesses were less in the nasal and temporal macula. Relative sparing of the ONL+ and/or OS layers was detected in the nasal (i.e., peripapillary) macula in 8 of 13 patients with extramacular disease on FAF; relative functional sparing was also detected in this subgroup. All 14 patients with disease confined to the central macula, as detected on FAF, showed ONL+ and OS layer thinning in regions of normal RPE thickness. CONCLUSIONS Relative peripapillary sparing was detected in STGD1 patients with extramacular disease on FAF. Photoreceptor thinning may precede RPE degeneration in STGD1.
Expanding the Algorithmic Information Theory Frame for Applications to Earth Observation

Recent years have witnessed an increased interest towards compression-based methods and their applications to remote sensing, as these have a data-driven and parameter-free approach and can be thus succesfully employed in several applications, especially in image information mining. This paper expands the algorithmic information theory frame, on which these methods are based. On the one hand, algorithms originally defined in the pattern matching domain are reformulated, allowing a better understanding of the available compression-based tools for remote sensing applications. On the other hand, the use of existing compression algorithms is proposed to store satellite images with added semantic value.
Vassiliev Invariants and the Loop States in Quantum Gravity

An exposition of Vassiliev invariants is given in terms of the simplest approach to the functional integral construction of link invariants from Chern-Simons theory. This approach gives the top row evaluations of Vassiliev invariants for the classical Lie algebras, and a neat point of view on the results of Bar-Natan. It also clarifies the relation between Vassiliev invariants and the extension of the bracket invariant to links with transverse double points that appears in the work of Bruegmann, Gambini and Pullin on the loop representation of quantum gravity. We see that the Vassiliev vertex is not just a transversal intersection of Wilson loops, but rather has the structure of Casimir insertion (up to first order of approximation) coming from the difference formula in the functional integral. (Figures are available from the author.)
Retinal Remodeling and Metabolic Alterations in Human AMD

Age-related macular degeneration (AMD) is a progressive retinal degeneration resulting in central visual field loss, ultimately causing debilitating blindness. AMD affects 18% of Americans from 65 to 74, 30% older than 74 years of age and is the leading cause of severe vision loss and blindness in Western populations. While many genetic and environmental risk factors are known for AMD, we currently know less about the mechanisms mediating disease progression. The pathways and mechanisms through which genetic and non-genetic risk factors modulate development of AMD pathogenesis remain largely unexplored. Moreover, current treatment for AMD is palliative and limited to wet/exudative forms. Retina is a complex, heterocellular tissue and most retinal cell classes are impacted or altered in AMD. Defining disease and stage-specific cytoarchitectural and metabolic responses in AMD is critical for highlighting targets for intervention. The goal of this article is to illustrate cell types impacted in AMD and demonstrate the implications of those changes, likely beginning in the retinal pigment epithelium (RPE), for remodeling of the the neural retina. Tracking heterocellular responses in disease progression is best achieved with computational molecular phenotyping (CMP), a tool that enables acquisition of a small molecule fingerprint for every cell in the retina. CMP uncovered critical cellular and molecular pathologies (remodeling and reprogramming) in progressive retinal degenerations such as retinitis pigmentosa (RP). We now applied these approaches to normal human and AMD tissues mapping progression of cellular and molecular changes in AMD retinas, including late-stage forms of the disease.
Tangible Sound: Musical Instrument Using Fluid Media

In this paper, we introduce “ Tangible Sound,” a musical instrument with a novel user interface that uses fluid water. Like music, fluids cannot be physically grasped because their shape is constantly changing. We thus believe that water is a suitable interface for performing flowing music. We created a live, hands-on installation that uses the flow of water as an input medium to control the intuitively appealing feeling of musical flow. With our instrument, performers interact with water flowing from a faucet into a drain. We have developed a method for measuring the volume of the water flow and for generating music from this measurement. This installation leads to the novel concept of “ Source and Drains” for programmable musical instruments. Finally, we consider the potential of this special interface for musical instruments and interactive arts.
Algorithm selection for classification problems

A number of algorithms are available in the areas of data mining, machine learning and pattern recognition for solving the same kind of problem. But there is a little guidance for suggesting algorithm to use which gives best results for the problem at hand. This paper shows an approach for solving this problem using meta-learning. The paper uses three types of data characteristics. Simple, information theoretic, and statistical data characteristics are used. Results are generated using nine different algorithms on thirty eight benchmark datasets from UCI repository. The proposed approach uses K-nearest neighbor algorithm for suggesting the suitable algorithm. Classifier accuracy is taken as a basis for recommending the algorithm. By using meta-learning, accurate method can be recommended as per the given data, and cognitive overload for applying each method, comparing with other methods and then selecting the suitable method for use can be reduced. Thus it helps in adaptive learning methods. The experimentation shows that predicted accuracies are matching with the actual accuracies for more than 90 % of the benchmark datasets used. Thus it is concluded that the number of attributes, the number of instances, the number of classes, maximum probability of class and class entropy are playing a major role in classifier accuracy and algorithm selection for thirty eight datasets used for experimentation.