Miracle-dz/UniFAR-FTUs huggingface dataset:
Generating Referential Descriptions in Multimedia Environments

All known algorithms dedicated to the generation of referential descriptions use natural language alone to accomplish this communicative goal. Motivated by some limitations underlying these algorithms and the resulting restrictions in their scope, we attempt to extend the basic schema of these procedures to multimedia environments, that is, to descriptions consisting of images and text. We discuss several issues in this enterprise, including the transfer of basic ingredients to images and the hereby reinterpretation of language-specific concepts, matters of choice in the generation process, and the extended application potential in some typical scenarios. Moreover, we sketch our intended area of application, the identification of a particular object in the large visualization of mathematical proofs, which has some characteristic properties of each of these scenarios. Our achievement lies in extending the scope of techniques for generating referential descriptions through the incorporation of multimedia components and in enhancing the application areas for these techniques.
An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Hierarchical Self Organizing Map

—Image segmentation denotes a process of partitioning an image into distinct regions. A large variety of different segmentation approaches for images have been developed. Among them, the clustering methods have been extensively investigated and used. In this paper, a clustering based approach using a Self Organizing Map (SOM) algorithm is proposed for medical image segmentation. This paper describe segmentation method consists of two phases. In the first phase, the MRI brain image is acquired from patient database. In that film artifact and noise are removed. In the second phase (MR) image segmentation is to accurately identify the principal tissue structures in these image volumes. A new unsupervised MR image segmentation method based on fuzzy C-Means clustering algorithm for the Segmentation is presented
On Legal Inferentialism' Toward a Pragmatics of Semantic Content in Legal Interpretation?

In this paper we consider whether a pragmatics of semantic content can be a useful approach to legal interpretation. More broadly speaking, since a pragmatic conception of meaning is a component of inferential semantics, we consider whether an inferentialist approach to legal interpretation can be useful in dealing with some problems of this important aspect of law. In other words, we ask whether Legal Inferentialism is a suitable conception for legal interpretation. In Section 1 we briefly consider the semantics/pragmatics debate in contemporary philosophy of language and in relation to legal interpretation. In Section 2 we discuss the relations between a pragmatics of semantic content and an inferentialist conception of content. In Section 3 we consider how Inferentialism can be applied to legal interpretation. Finally, in Section 4 we consider some possible advantages and drawbacks of Inferentialism applied to legal interpretation and adjudication.
Leadership and role stressors as departmental level predictors of workplace bullying.

Environmental conditions have long been assumed to create climates that can encourage workplace bullying. Although several studies have supported this assumption, the vast majority have applied the individual as the unit of both measurement and analysis. We argue, however, that the appropriate level of inference regarding environmental conditions is the work-group. In a large sample of some 10,000 employees distributed across 685 departments, we tested the hypothesis that leadership practices and the presence of role stressors will predict the incidence of bullying within departments. The results showed leadership practices and role conflict to predict bullying at the departmental level, while role ambiguity did not when taking into account the effects of the other predictors. The robustness of the findings was demonstrated after excluding responses of targets of bullying, still showing leadership practices and role conflict as potent predictors of bullying, supporting the assumption that bullying will be prevalent within unfavorable working environments.
Spread of citrus huanglongbing (greening disease) following incursion into Papua New Guinea

Huanglongbing (HLB) was previously known in many countries as greening disease. It is caused by the bacterium ‘Candidates Liberibacter asiaticus’ and is vectored by the Asian citrus psyllid Diaphorina citri (Hemiptera: Psyllidae). A delimiting survey showed that the disease had become established in, and near the border town of, Vanimo in the Sandaun Province of Papua New Guinea (PNG) by late 2002. A campaign of quarantine containment and public awareness followed. A second survey undertaken 1 year later indicated that long-distance movement of the disease and its vector had not occurred. Out of a total of 120 trees indexed by polymerase chain reaction (PCR), 4/72 were HLB-positive in 2002 compared to 12/48 in 2003. The second survey found presumptive evidence for limited HLB disease cluster expansion and further independent introduction of infected planting material. HLB-positive samples were also screened for Citrus tristeza virus (CTV) infection using a double antibody sandwich enzyme-linked immunosorbent assay. The symptoms observed on leaves of orange (Citrus sinensis) and lemon (Citrus Union) infected by ‘Ca. L. asiaticus’ alone and ‘Ca. L. asiaticus’ plus CTV were similar. This is also the first verified record of CTV in PNG. No evidence was found for the presence of HLB in four Pacific Island countries (Cook Islands, Fiji Islands, Samoa and Tonga) to the east of PNG with 18 citrus trees tested negative by PCR.
These are not the Stereotypes You are Looking For: Bias and Fairness in Authorial Gender Attribution

Stylometric and text categorization results show that author gender can be discerned in texts with relatively high accuracy. However, it is difficult to explain what gives rise to these results and there are many possible confounding factors, such as the domain, genre, and target audience of a text. More fundamentally, such classification efforts risk invoking stereotyping and essentialism. We explore this issue in two datasets of Dutch literary novels, using commonly used descriptive (LIWC, topic modeling) and predictive (machine learning) methods. Our results show the importance of controlling for variables in the corpus and we argue for taking care not to overgeneralize from the results.
Congestion control in disruption-tolerant networks: a comparative study for interplanetary networking applications

Controlling congestion is critical to ensure adequate network operation and performance. That is especially the case in networks operating in "challenged"- or "extreme" environments where episodic connectivity is part of the network's normal operation. Our goal is to study congestion control mechanisms that have been proposed for these so-called disruption tolerant networks, or DTNs. In this paper, we conduct a performance study comparing existing DTN congestion control mechanisms for the specific case of inter-planetary networking (IPN) applications. Our results indicate that congestion control helps increase message delivery ratio, even in highly congested network scenarios. Our study also suggests that good design principles for congestion control in IPN scenarios include: using a combination of reactive- and proactive control, using local information instead of relying on global knowledge, and employing mechanisms that are routing protocol independent.
Effect of an increase in the plasma potassium concentration on renal magnesium handling in healthy volunteers.

BACKGROUND Lower plasma magnesium concentrations are associated with clinical problems such as arrhythmias and hypertension. Plasma magnesium concentration is tightly controlled by the kidney. Modifying renal magnesium threshold may provide a means to increase the plasma magnesium concentration. Since evidence has been presented that potassium deficiency by itself may increase renal magnesium loss, the hypothesis that elevating plasma potassium would result in an increase in plasma magnesium concentration was tested in healthy volunteers. METHODS Plasma potassium was raised in nine healthy volunteers by oral administration of 20 mg amiloride daily during 3 weeks. Magnesium metabolism was assessed before and after this period by plasma levels, urinary magnesium excretion and fractional magnesium excretion, and magnesium loading test (MLT). This MLT allows calculation of renal retention of a magnesium load. RESULTS Basal plasma magnesium levels (0.84 +/- 0.07 vs 0.84 +/- 0.05 mmol/l) as well as urinary magnesium excretion (4.37 +/- 1.73 vs 3.67 +/- 1.37 mmol/day) and erythrocyte magnesium levels (1.72 +/- 0.16 vs 1.76 +/- 0.14 mmol Mg/l red blood cells) were similar before and on amiloride. Plasma potassium rose significantly on amiloride (3.64 +/- 0.24 vs 4.07 +/- 0.54 mmol/l, P < 0.05). No change was observed in magnesium retention with the MLT: 22.7 +/- 26.7 vs 29.2 +/- 20.6% (P = 0.5). CONCLUSIONS Despite an increased plasma potassium concentration, no change was observed in plasma magnesium levels, urinary magnesium excretion or renal magnesium retention of an intravenously administered magnesium load. This indicates that increasing plasma potassium within the normal range does not modify the renal magnesium threshold.
Phase noise in bipolar and CMOS VCO's - an analytical comparison

As RF IC design has become more prevalent and more demanding, the need for a more precise VCO phase noise model has arisen. For both MOS and bipolar VCO's new formulas are still being proposed to model the phase noise, which is a key factor for a precise oscillator. In this paper, a previous phase noise model for a bipolar VCO as presented in A. Tasic and W. A. Serdijn (2002) is refuted and the formulas developed for MOS VCO phase noise calculations based in J. J. Rael and A. Abidi (2000) are adapted to bipolar VCO. Then, the results are compared and some conclusions are drawn for the comparison of these oscillators
Vitamin A deficiency alters the expression of mucin genes by the rat ocular surface epithelium.

PURPOSE To study effects of depletion of retinoic acid on expression of the mucins ASGP (rMuc4), rMuc5AC, and rMuc1, by the corneal and conjunctival epithelia of the rat. METHODS Nineteen-day-old Sprague-Dawley male rats were fed a casein-based vitamin A- deficient diet or casein-based diet with vitamin A as control. Rats from both groups were killed at 1, 3, 5, 13, 15, 18, and 20 weeks after initiation of feeding. Expression of the three mucin genes by the ocular surface epithelium was assayed by reverse transcription-polymerase chain reaction (RT-PCR) and in situ hybridization. RESULTS In vitamin A-deficient rats, ASGP mRNA was not detected by RT-PCR after 15 weeks of feeding. rMuc5AC mRNA was detected by RT-PCR at 15 weeks, but by 18 and 20 weeks was no longer detectable. By in situ hybridization, ASGP mRNA was localized in the entire ocular surface epithelium after 1 week of feeding, was diminished but detectable above background by 13 weeks, and was not detectable at 20 weeks. rMuc5AC mRNA was detected in the goblet cells of vitamin A- deficient rats by in situ hybridization at 13 weeks, but was lost by 20 weeks, as were identifiable goblet cells. rMuc1 mRNA were detected by RT-PCR through all time points of 1 to 20 weeks in both vitamin A-deficient and control rats, indicating no significant change in rMuc1 mRNA expression with vitamin A deficiency. CONCLUSIONS Both the membrane-spanning mucin ASGP (rMuc4) and the secretory mucin rMuc5AC are directly or indirectly regulated by vitamin A in the ocular surface epithelium, whereas the membrane-spanning mucin rMuc1 is not.
An Effective Network Partitioning Algorithm Based on Two-Point Diffusing Strategy

The network modeling and analysis have played important roles in fields of physics, sociology, biology, and computer science. Recently, community structure has been considered as an important character for complex networks, and its detection can bring great benefit in real world affairs. In the paper, a new heuristic algorithm based on two-point diffusing strategy is proposed. At first, two pseudo-core points are identified according to the clue of the longest path in a network. Then, two embryonic communities and an undecided node set are generated through performing diffusing operation on such two points. Subsequently, an experience rule is used to classify the undecided nodes to form the final community structure. In addition, the effectiveness and efficiency are validated by comparison experiments with four real-world networks. The experiment results show that our TPD algorithm can yield better community partition results and shorter computing time than the existing classical community detecting algorithms.
Vestibular disorders in children

When vertigo and dizziness are diagnosed in a child, this is a matter of concern for the physician and the child's family. Prior to a comprehensive otological, neurological, and vestibular clinical examination, physicians often request CT or MRI scanning, although in most cases such expensive testing is unnecessary. The present work is based on the results of a 14-year study conducted with a sample of more than 2,000 children referred for vertigo and balance disorders to the functional vestibular evaluation unit of the ENT pediatric department at Robert Debré Hospital (Paris). The clinical signs of vestibular deficit and the most frequent aetiologies of vertigo and dizziness in children are detailed (migraine equivalent, ophthalmologic disorders, benign paroxysmal idiopathic pediatric vertigo, and temporal bone fracture), which has been helpful in determining the best diagnostic procedure and therapy for vertigo in children.
Comunicación y conflicto familiar durante la adolescencia

The aim of this paper is to study the pattern of family communication and conflict during adolescence. A sample of 221 boys and 292 girls aged between 13 and 19, were surveyed about frequency of communication with both parents, frequency of conflicts, emotional intensity of these conflicts and their functional autonomy. The article presents some interesting results. On the one hand it reflects a less negative and less conflictive image of adolescence, due adolescents stating that they do not argue with their parents frequently. On the other hand, our results show important gender differences: girls talk more with their parents, have less arguments with them and are less autonomous than boys. Through adolescence, frequency of family conflict decreases slightly, and family communication increases, mainly for girls. Finally, our results identify an interesting relationship between frequency of conflict and emotional intensity.
Monitoring Suicidal Patients in Primary Care Using Electronic Health Records

Introduction: Patients at risk for suicide often come into contact with primary care providers, many of whom use electronic health records (EHRs) for charting. It is not known, however, how often suicide ideation or attempts are documented in EHRs. Methods: We used retrospective analyses of de-identified EHR data from a distributed health network of primary care organizations to estimate the frequency of using diagnostic codes to record suicidal ideation and attempts. Data came from 3 sources: a clinician notes field processed using natural language processing; a suicidal ideation item on a patient-reported depression severity instrument (9-item Patient Health Questionnaire [PHQ-9]); and diagnostic codes from the EHR. Results: Only 3% of patients with an indication of suicidal ideation in the notes field had a corresponding International Classification of Diseases, 9th Revision (ICD-9), code (κ = 0.036). Agreement between an indication of suicidal ideation from item 9 of the PHQ-9 and an ICD-9 code was slightly higher (κ = 0.068). Suicide attempt indicated in the notes field was more likely to be recorded using an ICD-9 code (19%; κ = 0.18). Conclusions: Few cases of suicidal ideation and attempt were documented in patients' EHRs using diagnostic codes. Increased documentation of suicidal ideation and behaviors in patients' EHRs may improve their monitoring in the health care system.
PERIORAL BIOMECHANICS, KINEMATICS, AND ELECTROPHYSIOLOGY IN PARKINSON'S DISEASE

This investigation quantitatively characterized the orofacial biomechanics, labial kinematics, and associated electromyography (EMG) patterns in individuals with Parkinson’s disease (PD) as a function of anti-PD medication state. Passive perioral stiffness, a clinical correlate of rigidity, was sampled using a face-referenced OroSTIFF system in 10 mildly diagnosed PD and 10 age/sex-matched control elderly. Labial movement amplitudes and velocities were evaluated using a 4-dimensional computerized motion capture system. Associated perioral EMG patterns were sampled to examine the characteristics of perioral muscles and compensatory muscular activation patterns during repetitive syllable productions. This study identified several trends that reflect various characteristics of perioral system differences between PD and control subjects: 1. The presence of high tonic EMG patterns after administration of dopaminergic treatment indicated an up-regulation of the central mechanism, which may serve to regulate orofacial postural control. 2. Multilevel regression modeling showed greater perioral stiffness in PD subjects, confirming the clinical correlate of rigidity in these patients. 3. Similar to the clinical symptoms in the upper and lower limb, a reduction of range of motion (hypokinesia) and velocity (bradykinesia) was evident in the PD orofacial system. Administration of dopaminergic treatment improved hypokinesia and
Effects on communication from intensive treatment with semantic feature analysis in aphasia

Background: Anomia, a persistent and frequent symptom of aphasia after stroke, obstructs the transfer of information in conversation, which can have serious consequences for participation in everyday social interaction with significant others and in society. Aims: This study aimed to replicate the findings of Coelho and his colleagues, as well as Boyle, and Wambaugh and Ferguson regarding the use of semantic feature analysis (SFA) for individuals with aphasia after stroke. Its main purpose was to explore whether intervention could (1) increase the ability to participate in conversation and (2) lead to positive changes in functional communication as perceived by the participants themselves and their significant others. Methods & Procedures: In this multiple-baseline across-subjects-design study, three participants with chronic aphasia were treated with intensive training using SFA. Treatment outcome was assessed with repeated measures of confrontation naming of actions and objects and with quantitative ratings of the quality of speech as well as communicative participation in conversation. The ratings were made by independent assessors blinded as to what phase of the study the data were obtained from. In addition, a questionnaire measuring perceived functional communication as reported by the participants and their significant others was administered before and after the training as well as at a follow-up session 10–12 weeks after the training was completed. Outcome & Results: Only minor treatment outcomes were apparent in this study. Visual inspection of the results showed a slight increase in the ability to participate in conversation for two of the three participants as well as a fall in the number of complex paraphasias for one of them and an increase in self-corrections for two. There was no improvement in the participants’ confrontation-naming ability. Two of the three participants rated their own functional communication skills higher at follow-up than before training while the third participant reported no change. As regards the ratings by significant others at follow-up versus before training, there was one slight decrease, one slight increase, and one considerable increase. Conclusions: Treatment with SFA might improve communicative skills and increase participation in everyday conversation despite not having a measurable effect on confrontation-naming ability in a formal assessment situation. Further research is needed to identify the individuals with aphasia who can benefit from treatment with SFA and to determine the nature of its impact on communicative participation.
Hybrid Structure-Adaptive RBF-ELM Network Classifier

In this paper, a hybrid structure-adaptive radial basis function-extreme learning machine (HSARBF-ELM) network classifier is presented. HSARBF-ELM consists of a structure-adaptive radial basis function (SARBF) network and an extreme learning machine (ELM) network of cascade, where the output of the SARBF network hidden layer is used as the input layer of the ELM network. In the HSARBF-ELM network classifier, the SARBF network is utilized to achieve adaptively localizing kernel mapping of input vectors, after that step, the ELM network is utilized to implement global classification of mapping samples in the kernel space. HSARBF-ELM indicates the combination of localized kernel mapping learning and the global nonlinear classification, which combines the advantages of the SARBF network and the ELM network. The quantitative conditions for the separability enhancement and the corresponding theoretical explanation for the HSARBF-ELM network are given, which demonstrate that when input vectors go through the SARBF network, adaptively adjusting the RBF kernel parameters can boost the separability of the original sample space. Thus, the classification performance of the HSARBF-ELM network can be guaranteed theoretically. An appropriate learning algorithm for the HSARBF-ELM network is subsequently presented, which effectively combines the methods of density clustering with a potential function, center-oriented unidirectional repulsive force and the existing ELM algorithm, and the optimized complementary HSARBF-ELM network can be constructed. The experimental results show that the classification performance of the HSARBF-ELM network clearly outperforms the ELM network, and outperforms other classifiers on most classification problems.