Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Semantic Textual Similarity from Conversations

We present a novel approach to learn representations for sentence-level semantic similarity using conversational data. Our method trains an unsupervised model to predict conversational responses. The resulting sentence embeddings perform well on the Semantic Textual Similarity (STS) Benchmark and SemEval 2017’s Community Question Answering (CQA) question similarity subtask. Performance is further improved by introducing multitask training, combining conversational response prediction and natural language inference. Extensive experiments show the proposed model achieves the best performance among all neural models on the STS Benchmark and is competitive with the state-of-the-art feature engineered and mixed systems for both tasks.
Phrase-Based & Neural Unsupervised Machine Translation

Machine translation systems achieve near human-level performance on some languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences, which hinders their applicability to the majority of language pairs. This work investigates how to learn to translate when having access to only large monolingual corpora in each language. We propose two model variants, a neural and a phrase-based model. Both versions leverage a careful initialization of the parameters, the denoising effect of language models and automatic generation of parallel data by iterative back-translation. These models are significantly better than methods from the literature, while being simpler and having fewer hyper-parameters. On the widely used WMT’14 English-French and WMT’16 German-English benchmarks, our models respectively obtain 28.1 and 25.2 BLEU points without using a single parallel sentence, outperforming the state of the art by more than 11 BLEU points. On low-resource languages like English-Urdu and English-Romanian, our methods achieve even better results than semi-supervised and supervised approaches leveraging the paucity of available bitexts. Our code for NMT and PBSMT is publicly available.
Non-uniformity normalization using 3 D Canny edges and Legendre polynomial approximation of the bias field : validation on 7 T T 1 W brain images

MR signal intensity, especially at high field strength, is affected by inhomogeneity, or shading artefacts, manifested as a smooth spatially varying signal intensity distortions. Nonuniformity is attributed to inhomogeneous RF fields, inhomogeneous reception sensitivity and electromagnetic interaction with the object being scanned [1]. Correction of this effect is the key to successful implementation of all MR image analyses, including segmentation, registration and functional modeling of dynamic data. We have developed a new method BiCal (Bias Calculation) for non-uniformity correction that uses two very general assumptions: a) bias field is multiplicative (algorithm can be trivially modified for the additive field) b) local signal intensity variation within a homogeneous tissue are caused by the bias field.
Adherence to treatment guidelines in clinical practice: study of antipsychotic treatment prior to clozapine initiation

Background Clozapine is the only antipsychotic drug licensed for treatment-resistant schizophrenia but its use is often delayed. Since previous studies, national guidelines on the use of clozapine and other antipsychotics have been disseminated to clinicians. Aims To determine the theoretical delay to clozapine initiation and to quantify the prior use of antipsychotic polypharmacy and high-dose antipsychotic treatment. Method Clinico-demographic data were extracted from the treatment records of all patients commencing clozapine in our centre between 2006 and 2010. Results Complete records were available for 149 patients. The mean theoretical delay in initiating clozapine was 47.7 months (s.d. = 49.7). Before commencing clozapine, antipsychotic polypharmacy and high-dose treatment was evident in 36.2 and 34.2% of patients respectively. Theoretical delay was related to illness duration (β = 0.7, P<0.001) but did not differ by gender or ethnicity. Conclusions Substantial delays to clozapine initiation remain and antipsychotic polypharmacy and high doses are commonly used prior to clozapine, despite treatment guidelines.
Silicon Promotes Growth of Brassica napus L. and Delays Leaf Senescence Induced by Nitrogen Starvation

Silicon (Si) is the second most abundant element in soil and has several beneficial effects, especially in plants subjected to stress conditions. However, the effect of Si in preventing nitrogen (N) starvation in plants is poorly documented. The aim of this work was to study the effect of a short Si supply duration (7 days) on growth, N uptake, photosynthetic activity, and leaf senescence progression in rapeseed subjected (or not) to N starvation. Our results showed that after 1 week of Si supply, Si improves biomass and increases N uptake and root expression of a nitrate transporter gene. After 12 days of N starvation, compared to -Si plants, mature leaf from +Si plants showed a high chlorophyll content, a maintain of net photosynthetic activity, a decrease of oxidative stress markers [hydrogen peroxide (H2O2) and malondialdehyde (MDA)] and a significant delay in senescence. When N-deprived plants were resupplied with N, a greening again associated with an increase of photosynthetic activity was observed in mature leaves of plants pretreated with Si. Moreover, during the duration of N resupply, an increase of N uptake and nitrate transporter gene expression were observed in plants pretreated with Si. In conclusion, this study has shown a beneficial role of Si to alleviate damage associated with N starvation and more especially its role in delaying of leaf senescence.
A Novel Cascading Faults Graph Based Transmission Network Vulnerability Assessment Method

This paper proposes a novel cascading faults graph (CFG) based on fault chain theory to assess the transmission network vulnerability. The proposed CFG is able to effectively reveal the mechanism of fault propagation in a transmission network by transforming an electrical network with spatial information into a fault propagation graph with temporal information, thus providing a better way for transmission network vulnerability assessment. Numerical results on IEEE 39-, 118-bus systems and French grid demonstrate the effectiveness of the proposed method.
In vitro Evaluation of Copper Tolerance and Accumulation in Populus nigra

Phytoextraction is an efficient and cheap way to extract copper from soils in riparian zones. In this work five genotypes of the endangered tree species Populus nigra L. were tested for their copper tolerance and accumulation in vitro when cultivated on media with three Cu concentrations: 10 -3 , 10 -4 and 10 -7 M (buffered with citric acid/Na-citrate buffer, pH 3 before sterilization). After five-weeks cultivation of rooted shoots, the highest increases in morphological and biomass parameters were observed at 10 -7 M Cu 2+ . As the medium with 10 -3 M Cu 2+ exhibited a toxic effect, the effect of 10 -4 M Cu 2+ and pH 3 was used for further genotype evaluation. According to the measured morphological and parameters of photosynthetic pigment contents, the best performance was achieved by the genotype Populus nigra cl. DN3. The highest copper accumulation on the same medium was achieved by genotype Populus nigra cl. BN5. The obtained data point to the considerable potential of the applied method in the evaluation of Populus nigra genotypes for use in projects of copper phytoextraction. https://doi.org/10.2298/ABS170210014V Received: February 10, 2017; Revised: April 6, 2017; Accepted: April 26, 2017; Published online: May 15, 2017 How to cite this article: Vuksanovic V, Kovacevic B, Katanic M, Orlovic S, Miladinovic D. In vitro evaluation of copper tolerance and accumulation in Populus nigra . Arch Biol Sci. 2017;69(4):679-87.
Resolution of Right Hemidiaphragm Paralysis following Cervical Foraminotomies

Introduction Hemidiaphragm paralysis secondary to phrenic nerve palsy is a well-recognised medical condition. There are few case reports in the literature documenting resolution of hemidiaphragm paralysis following cervical spine surgery. This case report documents our experience with one such case. Case Presentation A 64-year-old man was referred to the orthopaedic service with right hemidiaphragm paralysis. He had a previous history of asbestos exposure and polio and was initially seen and investigated by the respiratory physicians. He also reported intermittent neck pain and an MRI scan showed right-sided cervical foraminal stenosis. He underwent posterior right C3/4 and C4/5 foraminotomies, and by three months postoperatively, his hemidiaphragm paralysis had resolved and his shortness of breath had also improved. Conclusion This report documents a unique case of resolution of hemidiaphragm paralysis following posterior unilateral cervical foraminotomies.
Incidence of diabetes in youth in the United States.

CONTEXT Data on the incidence of diabetes mellitus (DM) among US youth according to racial/ethnic background and DM type are limited. OBJECTIVE To estimate DM incidence in youth aged younger than 20 years according to race/ethnicity and DM type. DESIGN, SETTING, AND PARTICIPANTS A multiethnic, population-based study (The SEARCH for Diabetes in Youth Study) of 2435 youth with newly diagnosed, nonsecondary DM in 2002 and 2003, ascertained at 10 study locations in the United States, covering a population of more than 10 million person-years. MAIN OUTCOME MEASURE Incidence rates by age group, sex, race/ethnicity, and DM type were calculated per 100,000 person-years at risk. Diabetes mellitus type (type 1/type 2) was based on health care professional assignment and, in a subset, further characterized with glutamic acid decarboxylase (GAD65) autoantibody and fasting C peptide measures. RESULTS The incidence of DM (per 100,000 person-years) was 24.3 (95% confidence interval [CI], 23.3-25.3). Among children younger than 10 years, most had type 1 DM, regardless of race/ethnicity. The highest rates of type 1 DM were observed in non-Hispanic white youth (18.6, 28.1, and 32.9 for age groups 0-4, 5-9, and 10-14 years, respectively). Even among older youth (> or =10 years), type 1 DM was frequent among non-Hispanic white, Hispanic, and African American adolescents. Overall, type 2 DM was still relatively infrequent, but the highest rates (17.0 to 49.4 per 100,000 person-years) were documented among 15- to 19-year-old minority groups. CONCLUSIONS Our data document the incidence rates of type 1 DM among youth of all racial/ethnic groups, with the highest rates in non-Hispanic white youth. Overall, type 2 DM is still relatively infrequent; however, the highest rates were observed among adolescent minority populations.
Drug-induced parkinsonism: diagnosis and management

php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). Journal of Parkinsonism and Restless Legs Syndrome 2016:6 83–91 Journal of Parkinsonism and Restless Legs Syndrome Dovepress
Comparison of once-daily versus twice-daily dosing of valsartan in patients with chronic stable heart failure

Background: The safety of once-daily (qd) dosing of valsartan in heart failure (HF) patients is not known. Hypothesis: This 10-week, double-blind trial examined the relative safety and efficacy of valsartan administered qd versus twice-daily (bid). Methods: HF patients (NYHA class II–III) receiving diuretics (87%), angiotensin-converting enzyme inhibitors (98%), beta-blockers (92%), aldosterone antagonists (25%), or digoxin (32%) were randomized to valsartan 40 mg bid (n = 60) or 80 mg qd (n = 55) and titrated to a maximum dose of 320 mg/day; doubling the dose every 2 weeks. Clinical and biochemical parameters were measured at Weeks 2, 4, 6, and 10. Results: The average dose of valsartan at the end of study was 245 mg in the bid group vs 256 mg in the qd group (P = NS). Similar proportions of patients tolerated qd vs bid dosing (bid 67% vs qd 68%). Outcome measures including reduction in blood pressure, incidence of hypotension, renal impairment, orthostatic dizziness or fatigue, changes in serum K+, creatinine, cystatin-C, and estimated glomerular filtration rate were similar between the 2 groups at all time-points. Brain natriuretic peptide levels decreased and plasma renin activity increased from baseline by the same amount in both groups at all time-points. Conclusion: Valsartan administered qd has a similar safety and tolerability profile with comparable 24-hour RAAS blockade, as assessed by increases in PRA, as bid dosing in patients with moderate to severe (NYHA class II–III) heart failure.
Distinguishing Adolescents With Conduct Disorder From Typically Developing Youngsters Based on Pattern Classification of Brain Structural MRI

Background: Conduct disorder (CD) is a mental disorder diagnosed in childhood or adolescence that presents antisocial behaviors, and is associated with structural alterations in brain. However, whether these structural alterations can distinguish CD from healthy controls (HCs) remains unknown. Here, we quantified these structural differences and explored the classification ability of these quantitative features based on machine learning (ML). Materials and Methods: High-resolution 3D structural magnetic resonance imaging (sMRI) was acquired from 60 CD subjects and 60 age-matched HCs. Voxel-based morphometry (VBM) was used to assess the regional gray matter (GM) volume difference. The significantly different regional GM volumes were then extracted as features, and input into three ML classifiers: logistic regression, random forest and support vector machine (SVM). We trained and tested these ML models for classifying CD from HCs by using fivefold cross-validation (CV). Results: Eight brain regions with abnormal GM volumes were detected, which mainly distributed in the frontal lobe, parietal lobe, anterior cingulate, cerebellum posterior lobe, lingual gyrus, and insula areas. We found that these ML models achieved comparable classification performance, with accuracy of 77.9 ∼ 80.4%, specificity of 73.3 ∼ 80.4%, sensitivity of 75.4 ∼ 87.5%, and area under the receiver operating characteristic curve (AUC) of 0.76 ∼ 0.80. Conclusion: Based on sMRI and ML, the regional GM volumes may be used as potential imaging biomarkers for stable and accurate classification of CD.
nerve response to urethral pressure and flow Neurophysiological modeling of voiding in rats: urethral

[PDF] [Full Text] [Abstract] , June 1, 1999; 276 (6): R1819-R1824. Am J Physiol Regulatory Integrative Comp Physiol E. van Asselt, J. le Feber and R. van Mastrigt Threshold for efferent bladder nerve firing in the rat [PDF] [Full Text] [Abstract] , November 1, 1999; 277 (5): R1368-R1375. Am J Physiol Regulatory Integrative Comp Physiol J. le Feber and E. van Asselt Pudendal nerve stimulation induces urethral contraction and relaxation
Balanced-to-Balanced Microstrip Diplexer Based on Magnetically Coupled Resonators

Two balanced-to-balanced planar diplexers based on magnetically coupled microstrip resonators are proposed in this paper. For the first prototype, each channel/differential-output is composed of a second order single-band balanced bandpass filter based on open-loop resonators. For the second diplexer example, the filters composing the differential outputs are fourth order and are implemented by means of folded stepped-impedance resonators. The design procedure for the differential response is quite straightforward, since it is based on the use of the well-known external quality factor and coupling coefficients concepts. Common-mode is inherently rejected thanks to the benefits of magnetic coupling, which precludes common-mode transmission over a wide frequency range. The proposed structure also offers a high level of channel-to-channel isolation. To demonstrate the usefulness of the proposed idea, the two prototypes are simulated, fabricated, and measured. Good differential-mode and common-mode performance is observed in both examples. Simulations and measurements show good agreement.
Impact of Product Pictures and Brand Names on Memory of Chinese Metaphorical Advertisements

Researchers typically classify metaphorical advertisements in the printed media as contradictory associations (Leigh, 1994) or as destabilization tropes (McQuarrie & Mick, 1996). Although a metaphor is a kind of unstable trope model, the rhetorician believes that a metaphor contains strong persuasive power (McQuarrie & Mick, 1996; Morgan & Reichert, 1999). MacInnis, Moorman, and Jaworski (1991) have suggested that metaphorical advertisements tend to lead consumers to profoundly process the message being presented therein. Mick (1992) proposed a theoretical framework using levels of subjective comprehension (LSC) to explain the perspective of an advertisement’s message in relationship to the consumer’s comprehension. This theory explains that, the higher the level of advertising language understood by the consumer, the better the advertising memory will be. McQuarrie and Mick (2003) have reviewed research findings in relation to rhetorical advertisements for over fifteen years and have found that trope type advertisements not only enhance consumers’ advertising memory, but also strengthen their positive attitude toward a product. Barry (1987) has indicated that when consumers perform cognitive processing, their initial perception of a message’s content kicks off not only the cognitive process, but the memory process as well. Although some studies have demonstrated that metaphorical advertisements can easily arouse viewer motivation to perceive the advertising message, most studies have not contained in-depth analyses of the types of metaphorical rhetoric used or the necessary data to determine what kind of metaphorical language is most successful in stimulating consumers to gain a deeper perception of the advertised message. This establishes the initial focus of this study. Most print advertisements consist of three basic elements: a visual element (a picture), a language element (words), and a brand name element (the merchandise). The establishment of correlations and connections among these elements serves to improve the memory of the consumer. At the same time, the advertiser can strengthen the consumer’s memory of the merchandise being advertised by exploiting the relationship of picture, words and brand name in accordance with clues (Schmitt, Tavassoli, & Millard, 1993). The dual loop theory proposed by Rossiter and Percy (1980) expounds on the visual loop and verbal loop, which can be processed through visual imagery and verbal belief, respectively, and which can influence the attitudes of consumers. Edell and Staelin (1983) have shown that consumers tend to recall an advertisement more easily when
A Dual Monte Carlo Approach to Estimate Model Uncertainty and its Application to the Rangeland Hydrology and Erosion Model

Natural resources models serve as important tools to support decision making by predicting environmental indicators. All model predictions have uncertainty associated with them. Model predictive uncertainty, often expressed as the confidence interval around a model prediction value, may serve as important supplementary information for assisting decision making processes. In this article, we describe a new method called Dual Monte Carlo (DMC) to calculate model predictive uncertainty based on input parameter uncertainty. DMC uses two Monte Carlo sampling loops, which enable model users to not only calculate the model predictive uncertainty for selected input parameter sets of particular interest, but also to examine the predictive uncertainty as a function of model inputs across the full range of parameter space. We illustrate the application of DMC to the process-based, rainfall event-driven Rangeland Hydrology and Erosion Model (RHEM). The results demonstrate that DMC effectively generated model predictive uncertainty from input parameter uncertainty and provided information that could be useful for decision making. We found that for the model RHEM, the uncertainty intervals were strongly correlated to specific model input and output parameter values, yielding regression relationships (r2 > 0.97) that enable accurate estimation of the uncertainty interval for any point in the input parameter space without the need to run the Monte Carlo simulations each time the model is used. Soil loss predictions and their associated uncertainty intervals for three example storms and three site conditions are used to illustrate how DMC can be a useful tool for directing decision making.
Remote Sensing for Crop Management

with the Agricultural Research Service (ARS) and various government agencies and private institutions have provided a great deal of fundamental information relating spectral reflectance and thermal emittance properties of soils and crops to their agronomic and biophysical characteristics. This knowledge has facilitated the development and use of various remote sensing methods for non-destructive monitor- ing of plant growth and development and for the detection of many environmental stresses which limit plant productivity. Coupled with rapid advances in computing and position- locating technologies, remote sensing from ground-, air-, and space-based platforms is now capable of providing detailed spatial and temporal information on plant response to their local environment that is needed for site specific agricultural management approaches. This manuscript, which empha- sizes contributions by ARS researchers, reviews the biophysi- cal basis of remote sensing; examines approaches that have been developed, refined, and tested for management of water, nutrients, and pests in agricultural crops; and as- sesses the role of remote sensing in yield prediction. It con- cludes with a discussion of challenges facing remote sens- ing in the future.
State or trait: Effects of positive mood on prosocial behaviors at work.

Positive mood at work (as an affective state) was hypothesized to be significantly and positively associated with the performance of both extrarole and role-prescribed prosocial organizational behaviors. Moreover, positive mood was hypothesized to have effects on prosocial behavior above and beyond the effects of fairness cognitions. Conversely, positive mood as a trait (\£., positive affectivity) was expected to be unrelated to either form of prosocial behavior. Finally, the form of role-prescribed prosocial behavior investigated, customer-service behavior or helpful behavior directed at customers, was hypothesized to be positively associated with sales performance. These hypotheses were tested with a sample of 221 salespeople. All of the hypotheses were supported. Implications of these results and directions for future research are discussed.