Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-modal associations between materic painting and classical Spanish music

The study analyses the existence of cross-modal associations in the general population between a series of paintings and a series of clips of classical (guitar) music. Because of the complexity of the stimuli, the study differs from previous analyses conducted on the association between visual and auditory stimuli, which predominantly analyzed single tones and colors by means of psychophysical methods and forced choice responses. More recently, the relation between music and shape has been analyzed in terms of music visualization, or relatively to the role played by emotion in the association, and free response paradigms have also been accepted. In our study, in order to investigate what attributes may be responsible for the phenomenon of the association between visual and auditory stimuli, the clip/painting association was tested in two experiments: the first used the semantic differential on a unidimensional rating scale of adjectives; the second employed a specific methodology based on subjective perceptual judgments in first person account. Because of the complexity of the stimuli, it was decided to have the maximum possible uniformity of style, composition and musical color. The results show that multisensory features expressed by adjectives such as “quick,” “agitated,” and “strong,” and their antonyms “slow,” “calm,” and “weak” characterized both the visual and auditory stimuli, and that they may have had a role in the associations. The results also suggest that the main perceptual features responsible for the clip/painting associations were hue, lightness, timbre, and musical tempo. Contrary to what was expected, the musical mode usually related to feelings of happiness (major mode), or to feelings of sadness (minor mode), and spatial orientation (vertical and horizontal) did not play a significant role in the association. The consistency of the associations was shown when evaluated on the whole sample, and after considering the different backgrounds and expertise of the subjects. No substantial difference was found between expert and non-expert subjects. The methods used in the experiment (semantic differential and subjective judgements in first person account) corroborated the interpretation of the results as associations due to patterns of qualitative similarity present in stimuli of different sensory modalities and experienced as such by the subjects. The main result of the study consists in showing the existence of cross-modal associations between highly complex stimuli; furthermore, the second experiment employed a specific methodology based on subjective perceptual judgments.
Associations between maternal and child health status and patterns of medical care use.

OBJECTIVE To determine associations in health status and health care utilization between mothers and their children. METHODS Cross-sectional analysis of the 1996-97 Community Tracking Study Household Survey. Separate logistic regression models describing each type of service use were conducted and adjusted for child age, health status, mother age, race, education, and respondent type. Models incorporated maternal health care use, employment, poverty, child's insurance status, and family type as independent variables. RESULTS Sample included 9803 mother-child pairs, for a weighted sample of 35 651 048 pairs. Compared to mothers reporting excellent health, mothers reporting very good to good health status and fair or poor health were more likely to have children in good, fair, or poor health (odds ratio 2.00, 95% confidence interval 1.63-2.45; and odds ratio 4.16, 3.36-5.15, respectively). For each of 5 types of service use, mothers' and children's use were strongly associated: 1+ physician visits (2.42, 2.07-2.84); 6+ physician visits (2.07, 1.80-2.37); emergency department use (2.01, 1.75-2.31); hospitalizations (1.56, 1.10-2.22); and mental health visits (7.07, 5.67-8.82). CONCLUSIONS Associations in health service use were noted across a broad array of services for women and their children. These associations may reflect similar tendencies to seek care and suggest the need to consider patterns of maternal use in trying to understand and improve patterns of health care utilization for children.
Mechanical Data for Use in Damage Tolerance Analyses

This report describes the results of a research program to determine the damage tolerance properties of metallic propeller materials. Three alloys were selected for investigation: 2025-T6 Aluminum, D6AC Steel and 4340 Steel. Mechanical response, fatigue (S-N) and fatigue crack growth rate data are presented for all of the alloys. The main conclusions that can be drawn from this study are as follows. The damage tolerant design of a propeller system will require a complete understanding of the fatigue crack growth threshold. There exists no experimental procedure to reliably develop the fatigue crack growth threshold data that is needed for damage tolerant design methods. Significant research will be required to fully understand the fatigue crack growth threshold. The development of alternative precracking methods, evaluating the effect of specimen configuration and attempting to identify micromechanical issues are simply the first steps to understanding the mechanics of the threshold.
Aerobic training improves exercise-induced lipolysis in SCAT and lipid utilization in overweight men.

The aim of this study was to investigate whether endurance training improves lipid mobilization and oxidation in overweight subjects. Eleven young men (25.6 +/- 1.4 yr and body mass index 27.7 +/- 0.2) performed a 4-mo training program consisting of practicing aerobic exercise 5 days/wk. Before and after the training period, lipid oxidation was explored during a 60-min exercise at 50% of peak O2 consumption by use of indirect calorimetry. Lipid mobilization and antilipolytic alpha2-adrenoceptor effect were also studied using the microdialysis method in abdominal subcutaneous adipose tissue (SCAT). After training, plasma nonesterified fatty acid (NEFA) levels, at rest and during exercise, were significantly lower than before (P < 0.001). Lipolysis in SCAT was significantly higher after than before training. An antilipolytic alpha2-adrenoceptor effect in SCAT was underlined during exercise before training and disappeared after. The respiratory exchange ratio was lower after training, i.e., the percentage of lipid oxidation was higher only at rest. The amount of lipid oxidized was higher after training, at rest, and during exercise. Although exercise power was higher after training, the relative intensity was equivalent, as suggested by a similar increase in plasma catecholamine concentrations before and after training. In conclusion, 4-mo training in overweight men improved lipid mobilization through a decrease of antilipolytic alpha2-adrenoceptor effect in SCAT and lipid oxidation during moderate exercise. Training induced a decrease of blood NEFA, predicting better prevention of obesity.
Learning to grasp with parental scaffolding

Parental scaffolding is an important mechanism utilized by infants during their development. Infants, for example, pay stronger attention to the features of objects highlighted by parents and learn the way of manipulating an object while being supported by parents. In this paper, a robot with the basic ability of reaching for an object, closing fingers and lifting its hand lacks knowledge of which parts of the object affords grasping, and in which hand orientation should the object be grasped. During reach and grasp attempts, the movement of the robot hand is modified by the human caregiver's physical interaction to enable successful grasping. The object regions that the robot fingers contact first are detected and stored as potential graspable object regions along with the trajectory of the hand. In the experiments, we showed that although the human caregiver did not directly show the graspable regions, the robot was able to find regions such as handles of the mugs after its action execution was partially guided by the human. Later, this experience was used to find graspable regions of never seen objects. At the end, the robot was able to grasp objects based on the position of the graspable part and stored action execution trajectories.
Quantitative Magnetic Resonance Imaging Differences Between Alzheimer Disease With and Without Subcortical Lacunes

Previous reports showed that patients with Alzheimer disease (AD) frequently have coexisting vascular-related pathologies, such as cerebral infarcts and white matter lesions. The aim of this study was to determine the effects of subcortical lacunar infarcts on brain structure in patients with AD. Semi-automated tissue segmentation and volumetry of magnetic resonance imaging data were performed in 38 AD patients without lacunes (AD−L), 24 AD patients with subcortical lacunes (AD+L), and 40 age-matched cognitively healthy subjects without lacunes. The following tissue volumes were quantified, expressed as percentage of total intracranial volume: ventricular cerebrospinal fluid (CSF), sulcal CSF, cortical gray matter (GM), subcortical GM, white matter (WM), white matter signal hyperintensities (WMSH), lacunes, and hippocampus. There was no difference in the Mini-Mental State Examination between the two AD groups. AD+L patients compared with AD−L subjects had significantly greater volumes of WMSH and ventricular CSF spaces (as expected) but smaller sulcal CSF spaces and no significant increase in cortical GM atrophy (both unexpected). In the AD groups, ventricular CSF correlated inversely with cortical GM but not with WM; sulcal CSF correlated inversely with cortical GM and WM. Cognitive impairment was associated with sulcal CSF volume but not with volumes of WMSH or lacunes. In conclusion, the presence of subcortical lacunes in those with AD is associated with more WM lesions and ventriculomegaly but not with cortical atrophy.
Instructor-centric source code plagiarism detection and plagiarism corpus

Existing source code plagiarism systems focus on the problem of identifying plagiarism between pairs of submissions. The task of detection, while essential, is only a small part of managing plagiarism in an instructional setting. Holistic plagiarism detection and management requires coordination and sharing of assignment similarity -- elevating plagiarism detection from pairwise similarity to cluster-based similarity; from a single assignment to a sequence of assignments in the same course, and even among instructors of different courses. To address these shortcomings, we have developed Student Submissions Integrity Diagnosis (SSID), an open-source system that provides holistic plagiarism detection in an instructor-centric way. SSID's visuals show overviews of plagiarism clusters throughout all assignments in a course as well as highlighting most-similar submissions on any specific student. SSID supports plagiarism detection workflows; e.g., allowing student assistants to flag suspicious assignments for later review and confirmation by an instructor with proper authority. Evidence is automatically entered into SSID's logs and shared among instructors. We have additionally collected a source code plagiarism corpus, which we employ to identify and correct shortcomings of previous plagiarism detection engines and to optimize parameter tuning for SSID deployment. Since its deployment, SSID's workflow enhancements have made plagiarism detection in our faculty less tedious and more successful.
Selection Criteria and Current Issues in Liver Transplantation for Hepatocellular Carcinoma

Liver transplantation (LT) is an ideal treatment for hepatocellular carcinoma (HCC) because it not only resects HCCs but it also replaces the underlying damaged liver with normal tissue. However, the selection criteria are still a matter of debate. After the introduction of the Milan criteria, some expanded criteria focusing on tumor size and number have been proposed. In addition, new expanded criteria considering tumor biology have been proposed using tumor markers and 18F-fluorodeoxyglucose positron emission tomography. This review summarizes the selection criteria in LT for HCC and introduces current issues focusing on the treatment for hepatitis C virus infection and the significance of sarcopenia in this field.
A retrospective on software component quality models

The academic and commercial sectors have shown an interest in Component Based Software Development (CBSD) because devel-oping and using various component forms as building blocks can significantly enhance software based system development and use. The perspective of reduced development costs and shorter life-cycles acts as a motivation for this interest. The idea is to create high quality parts and join them together to form a functioning system. One of the most critical processes in CBSD is the selection of appropriate COTS components that meet the user? s require-ments. An important step in the component selection process is the evaluation of components using quality models. Current compo-nent selection approaches try to propose appropriate quality mod-els for the effective assessment of such components. These proposals attempt to define quality characteristics, attributes, and metrics which are specific to the particular nature of components and CBSD. This paper presents a retrospective on the current quality models proposed and are available for the evaluation of software components.
Data Mining: A prediction for performance improvement using classification

Now-a-days the amount of data stored in educational database increasing rapidly. These databases contain hidden information for improvement of students' performance. The performance in higher education in India is a turning point in the academics for all students. This academic performance is influenced by many factors, therefore it is essential to develop predictive data mining model for students' performance so as to identify the difference between high learners and slow learners student. In the present investigation, an experimental methodology was adopted to generate a database. The raw data was preprocessed in terms of filling up missing values, transforming values in one form into another and relevant attribute/ variable selection. As a result, we had 300 student records, which were used for by Byes classification prediction model construction. Keywords- Data Mining, Educational Data Mining, Predictive Model, Classification.
Performance Improvement of Ferrite-Assisted Synchronous Reluctance Machines Using Asymmetrical Rotor Configurations

This paper proposes novel ferrite-assisted synchronous reluctance machines (FA-SynRMs) having the merit of highly improved machine performance including torque, efficiency, and power factor. The particular superiority of the proposed FA-SynRMs is the utilization of an asymmetrical rotor configuration that contributes to the full use of torque components by allowing the magnetic and reluctance torques to reach their maximum values near or at the same current phase angles. To provide visible insights into the contribution, a frozen permeability method is utilized to separate the torque into the reluctance torque and the magnetic torque based on a 2-D finite-element method by the aid of JMAG-Designer. For highlighting the advantages of the proposed models, a conventional FA-SynRM with symmetrical rotor configurations is adopted for comparison under the same operating conditions. As a result, it is demonstrated that the proposed design is effective in improving the performance of FA-SynRMs.
Role of Extracellular Magnesium in Insulin Secretion from Rat Insulinoma Cells

Abstract Magnesium (Mg2+) is an abundant intracellular cation that participates in the regulation of the intracellular concentration of ATP. In this study, we examined the relationship between insulin secretion and intracellular free Mg2+ ([Mg2+]i) in a rat-insulinoma cell line (RIN m5F), using a fluorescent dye (Mag-fura-2). KCI, forskolin, and D-glyceraldehyde increased [Mg2+]i and insulin secretion from RIN m5F cells in a dose-dependent fashion. Verapamil, a voltage-dependent Ca2+ channel blocker, inhibited the increase of [Mg2+]i that was evoked by KCI, forskolin, and D-glyceraldehyde. In a Mg2+-free buffer, these agents failed to cause an elevation in [Mg2+]i; however, the insulin response to KCI and forskolin was enhanced, compared with that in the presence of Mg2+ (1.25 mM). Our findings suggest that [Mg2+]i is dependent upon extracellular Mg2+, and the influx through the voltage-dependent Ca2+ channel. Mg2+ may competitively inhibit the voltage-dependent Ca2+ channel, which is known to play a role in insulin secretion. An absence of Mg2+ in the extracellular space may result in enhanced insulin secretion. [Mg2+]i may play a role in insulin secretion from RIN m5F cells.
Metabolic Flux and Compartmentation Analysis in the Brain In vivo

Through significant developments and progresses in the last two decades, in vivo localized nuclear magnetic resonance spectroscopy (MRS) became a method of choice to probe brain metabolic pathways in a non-invasive way. Beside the measurement of the total concentration of more than 20 metabolites, 1H MRS can be used to quantify the dynamics of substrate transport across the blood-brain barrier by varying the plasma substrate level. On the other hand, 13C MRS with the infusion of 13C-enriched substrates enables the characterization of brain oxidative metabolism and neurotransmission by incorporation of 13C in the different carbon positions of amino acid neurotransmitters. The quantitative determination of the biochemical reactions involved in these processes requires the use of appropriate metabolic models, whose level of details is strongly related to the amount of data accessible with in vivo MRS. In the present work, we present the different steps involved in the elaboration of a mathematical model of a given brain metabolic process and its application to the experimental data in order to extract quantitative brain metabolic rates. We review the recent advances in the localized measurement of brain glucose transport and compartmentalized brain energy metabolism, and how these reveal mechanistic details on glial support to glutamatergic and GABAergic neurons.
Reciprocal Figures, Graphical Statics, and Inversive Geometry of the Schwarzian BKP Hierarchy

A remarkable connection between soliton theory and an important and beautiful branch of the theory of graphical statics developed by Maxwell and his contemporaries is revealed. Thus, it is demonstrated that reciprocal triangles that constitute the simplest pair of reciprocal figures representing both a framework and a self‐stress encapsulate the integrable discrete BKP equation and its Schwarzian version. The inherent Möbius invariant nature of the Schwarzian BKP equation is then exploited to define reciprocity in an inversive geometric setting. Integrable pairs of lattices of nontrivial combinatorics consisting of reciprocal triangles and their natural generalizations are discussed. Particular reductions of these BKP lattices are related to the integrable discrete versions of Darboux's (2+1)‐dimensional sine‐Gordon equation and the classical Tzitzéica equation of affine geometry. Furthermore, it is shown that octahedral figures and their hexahedral reciprocals as considered by Maxwell likewise give rise to discrete integrable systems and associated integrable lattices.
Knowledge-Based Data Analysis: First Step Toward the Creation of Clinical Prediction Rules Using a New Typicality Measure

Clinical prediction rules play an important role in medical practice. They expedite diagnosis and limit unnecessary tests. However, the rule creation process is time consuming and expensive. With the current developments of efficient data mining algorithms and growing accessibility to medical data, the creation of clinical rules can be supported by automated rule induction from data. A data-driven method based on the reuse of previously collected medical records and clinical trial statistics is cost-effective; however, it requires well defined and intelligent methods for data analysis. This paper presents a new framework for knowledge representation for secondary data analysis and for generation of a new typicality measure, which integrates medical knowledge into statistical analysis. The framework is based on a semiotic approach for contextual knowledge and fuzzy logic for approximate knowledge. This semio-fuzzy framework has been applied to the analysis of predictors for the diagnosis of obstructive sleep apnea. This approach was tested on two clinical data sets. Medical knowledge was represented by a set of facts and fuzzy rules, and used to perform statistical analysis. Statistical methods provided several candidate outliers. Our new typicality measure identified those, which were medically significant, in the sense that the removal of those important outliers improved the descriptive model. This is a critical preprocessing step towards automated induction of predictive rules from data. These experimental results demonstrate that knowledge-based methods integrated with statistical approaches provide a practical framework to support the generation of clinical prediction rules.
Gluing Grids and Clouds Together: A Service-Oriented Approach

Scientific communities are actively developing services to exploit the capabilities of service-oriented distributed systems. This exploitation requires services to be specified and developed for a range of activities such as querying, management and scheduling of workflows, image handling, provenance capture and management, amongst other activities. Most of these services are designed and developed for a particular community of scientific users. A key issue here is that the constraints imposed by architectures, interfaces or platforms can restrict or even prohibit the free interchange of services between disparate scientific communities who have a shared need for a particular service. Using the notion of "Platform as a Service" (PaaS), we propose an architectural approach that addresses these limitations so that that users can make use of a wider range of services without being concerned about the development of cross-platform middleware, wrappers or any need for bespoke applications in order to cross the boundaries between differing communities. The proposed architecture shields heterogeneous Grid and Cloud infrastructural detail with a brokering environment, thus enabling users to concentrate on the specification of higher level services that are more relevant to the intended application.
Activity Graphs of the Microstructure of Complex Work

1 ABSTRACT This poster presents activity graphs, an analytic approach to representing and understanding the microstructure of people’s activity patterns. Activity graphs help us to describe and interpret concepts such as multi-tasking, interruptions, and media use. This approach may be useful for research into activity management and interruption management. Preliminary results are presented as illustrations of the outcomes of this analytic approach.
DPX: for the analysis of the protein core

SUMMARY In order to obtain an accurate description of the protein interior, we describe a simple and fast algorithm that measures the depth of each atom in a protein (dpx), defined as its distance (A) from the closest solvent accessible atom. The program reads a PDB file containing the atomic solvent accessibility in the B-factor field, and writes a file in the same format, where the B-factor field now contains the dpx value. Output structure files can be thus directly displayed with molecular graphics programs like RASMOL, MOLMOL, Swiss-PDB View and colored according to dpx values. AVAILABILITY The algorithm is implemented in a standalone program written in C and its source is freely available at ftp.icgeb. trieste.it/pub/DPX or on request from the authors.