Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Track Authority and International Trade Negotiations

Fast Track Authority (FTA) is the institutional procedure in the Unites States whereby Congress grants to the President the power to negotiate international trade agreements. Under FTA, Congress can only approve or reject negotiated trade deals, with no possibility of amending them. In this paper, we examine the determinants of FTA voting decisions and the implications of this institutional procedure for trade negotiations. We describe a simple two-country trade model, in which industries are unevenly distributed across constituencies. In the foreign country, trade negotiating authority is delegated to the executive, while in the home country Congress can retain the power to amend trade agreements. We show that legislators' FTA voting behavior depends on the trade policy interests of their own constituencies as well as those of the majority of Congress. Empirical analysis of the determinants of all FTA votes between 1974 (when fast track was first introduced) and 2002 (when it was last granted) provides strong support for the predictions of our model.
Comments on A Parallel Mixture of SVMs for Very Large Scale Problems

Collobert, Bengio, and Bengio (2002) recently introduced a novel approach to using a neural network to provide a class prediction from an ensemble of support vector machines (SVMs). This approach has the advantage that the required computation scales well to very large data sets. Experiments on the Forest Cover data set show that this parallel mixture is more accurate than a single SVM, with 90.72 accuracy reported on an independent test set. Although this accuracy is impressive, their article does not consider alternative types of classifiers. We show that a simple ensemble of decision trees results in a higher accuracy, 94.75, and is computationally efficient. This result is somewhat surprising and illustrates the general value of experimental comparisons using different types of classifiers.
Exhaled Nitric Oxide in a Population Sample of Adults

Background: The relationship between exhaled nitric oxide (FENO) and the diagnosis of asthma in the general adult population is not completely clear. Objectives: To investigate the association between FENO and asthma, after controlling for atopy and rhinitis, in a general population sample of adults (mean age 43 years). Methods: The cohort of subjects was a sample of subjects who gave their consent to participate in the European Community Respiratory Health Survey II study. Results: Atopy, rhinitis and current asthma were positively and current smoking was negatively associated with FENO. Multivariate analysis showed that asthma had a significant predictive effect on FENO (β = 0.53; 95% CI 0.21–0.84, p = 0.001), and the increase in FENO was significantly associated with the risk of current asthma (OR = 1.07, 95% CI 1.00–1.14) by the logistic regression model. Receiver-operater characteristic curve showed that FENO ≧18.5 ppb had the best combination of sensitivity (69.2%) and specificity (71%), with a positive predictive value of 24% and a negative predictive value of 95%, for the diagnosis of asthma. Conclusions: Measuring FENO seems to be suitable as an adjunct to questionnaire in the evaluation of asthma in the general population.
Wireless Sensor Networks and the Internet of Things: Selected Challenges

Wireless sensor networks (WSNs) are increasingly gaining impact in our day to day lives. They are finding a wide range of applications in various domains, including health-care, assisted and enhanced-living scenarios, industrial and production monitoring, control networks, and many other fields. In future, WSNs are expected to be integrated into the “Internet of Things”, where sensor nodes join the Internet dynamically, and use it to collaborate and accomplish their tasks. However, when WSNs become a part of the Internet, we must carefully investigate and analyze the issues involved with this integration. In this paper, we evaluate different approaches to integrate WSNs into the Internet and outline a set of challenges, which we target to address in the near future.
Brother animal : the story of Freud and Tausk

A major figure among pre-World War I psychoanalysts, Victor Tausk was perhaps Sigmund Freud's most brilliant pupil--"the most prominently out-standing" in the opinion of Lou Andreas-Salome. Tausk craved recognition for the originality of his work, and a fierce rivalry developed between pupil and mentor. Tausk, who felt a deep and neurotic dependence on Freud, was totally consumed in the struggle. Freud's final rejection of his follower, and the particularly unfortunate manner in which it was carried out, was followed by Tausk's bizarre suicide--and by an official silence that has all but obliterated his name from the annals of psychoanalysis. Arthur Koestler called Brother Animal "A very important and original contribution to the history of the psychoanalytic movement and beyond that to the history of ideas," and Maxwell Geismar said it was "the best treatment of Freud and that remarkable group of his original disciples that 1 have read. It reads as if Mr. Roazen was right there at the time, inside the inner circle. "
Fourier Transform Infrared Spectroscopy for Rapid Identification of Nonfermenting Gram-Negative Bacteria Isolated from Sputum Samples from Cystic Fibrosis Patients

ABSTRACT The accurate and rapid identification of bacteria isolated from the respiratory tract of patients with cystic fibrosis (CF) is critical in epidemiological studies, during intrahospital outbreaks, for patient treatment, and for determination of therapeutic options. While the most common organisms isolated from sputum samples are Pseudomonas aeruginosa, Staphylococcus aureus, and Haemophilus influenzae, in recent decades an increasing fraction of CF patients has been colonized by other nonfermenting (NF) gram-negative rods, such as Burkholderia cepacia complex (BCC) bacteria, Stenotrophomonas maltophilia, Ralstonia pickettii, Acinetobacter spp. , and Achromobacter spp. In the present study, we developed a novel strategy for the rapid identification of NF rods based on Fourier transform infrared spectroscopy (FTIR) in combination with artificial neural networks (ANNs). A total of 15 reference strains and 169 clinical isolates of NF gram-negative bacteria recovered from sputum samples from 150 CF patients were used in this study. The clinical isolates were identified according to the guidelines for clinical microbiology practices for respiratory tract specimens from CF patients; and particularly, BCC bacteria were further identified by recA-based PCR followed by restriction fragment length polymorphism analysis with HaeIII, and their identities were confirmed by recA species-specific PCR. In addition, some strains belonging to genera different from BCC were identified by 16S rRNA gene sequencing. A standardized experimental protocol was established, and an FTIR spectral database containing more than 2,000 infrared spectra was created. The ANN identification system consisted of two hierarchical levels. The top-level network allowed the identification of P. aeruginosa, S. maltophilia, Achromobacter xylosoxidans, Acinetobacter spp. , R. pickettii, and BCC bacteria with an identification success rate of 98.1%. The second-level network was developed to differentiate the four most clinically relevant species of BCC, B. cepacia, B. multivorans, B. cenocepacia, and B. stabilis (genomovars I to IV, respectively), with a correct identification rate of 93.8%. Our results demonstrate the high degree of reliability and strong potential of ANN-based FTIR spectrum analysis for the rapid identification of NF rods suitable for use in routine clinical microbiology laboratories.
Small Regulatory RNA-Induced Growth Rate Heterogeneity of Bacillus subtilis

Isogenic bacterial populations can consist of cells displaying heterogeneous physiological traits. Small regulatory RNAs (sRNAs) could affect this heterogeneity since they act by fine-tuning mRNA or protein levels to coordinate the appropriate cellular behavior. Here we show that the sRNA RnaC/S1022 from the Gram-positive bacterium Bacillus subtilis can suppress exponential growth by modulation of the transcriptional regulator AbrB. Specifically, the post-transcriptional abrB-RnaC/S1022 interaction allows B. subtilis to increase the cell-to-cell variation in AbrB protein levels, despite strong negative autoregulation of the abrB promoter. This behavior is consistent with existing mathematical models of sRNA action, thus suggesting that induction of protein expression noise could be a new general aspect of sRNA regulation. Importantly, we show that the sRNA-induced diversity in AbrB levels generates heterogeneity in growth rates during the exponential growth phase. Based on these findings, we hypothesize that the resulting subpopulations of fast- and slow-growing B. subtilis cells reflect a bet-hedging strategy for enhanced survival of unfavorable conditions.
Fault tolerance for multi-threaded applications by leveraging hardware transactional memory

Providing fault tolerance especially to mission critical applications in order to detect transient and permanent faults and to recover from them is one of the main necessity for processor designers. However, fault tolerance for multi-threaded applications presents high performance degradations due to comparing the results of the instruction streams, checkpointing the entire system and recovering from the detected errors to an agreed state. In this study, we present FaulTM-multi, a fault tolerance scheme for multi threaded applications running on transactional memory hardware which reduces these performance degradations. FaulTM-multi decreases the performance degradation of lockstepping, a conventional fault detection scheme, from 23% and 9% to 10% and 2% for lock-based parallel and TM applications respectively. Also, FaulTM-multi creates 28% less checkpoints compared to Rebound, the state of the art checkpointing scheme.
Wider Context by Using Bilingual Language Models in Machine Translation

In past Evaluations for Machine Translation of European Languages, it could be shown that the translation performance of SMT systems can be increased by integrating a bilingual language model into a phrase-based SMT system. In the bilingual language model, target words with their aligned source words build the tokens of an n-gram based language model. We analyzed the effect of bilingual language models and show where they could help to better model the translation process. We could show improvements of translation quality on German-to-English and Arabic-to-English. In addition, for the Arabic-to-English task, training an extra bilingual language model on the POS tags instead of the surface word forms led to further improvements.
Watershed superpixel

As a pre-processing tool, superpixel algorithms have been popular used in many computer-vision applications. High efficiency is a desired property of superpixel algorithms, especially in real-time vision systems. In this paper, a novel high-efficient superpixel algorithm is developed based on the watershed algorithm, namely the spatial-constrained watershed (SCoW). SCoW performs watersheding in a marker-controlled manner, with a set of evenly placed markers. To align superpixel boundaries to image edges, an edge-preserving scheme is embedded into the SCoW which makes a balance between the homogeneity and the compactness. Without any complex computing, the proposed superpixel algorithm is found to produce high quality superpixels as traditional superpixel algorithms, while holding much higher efficiency.
Fracture Site Mobility at 6 Weeks After Humeral Shaft Fracture Predicts Nonunion Without Surgery

Objectives: To assess the presence of fracture site gross motion on physical examination to predict humeral shaft fracture progression to nonunion in patients managed nonoperatively. Design: Retrospective cohort study. Setting: Single trauma level 1 institutional center. Patients: Eighty-four consecutive patients undergoing nonoperative treatment of a diaphyseal humeral shaft fracture were identified. The average age of the population was 48.3 years, and 50% of the cohort was men. Intervention: Clinical examination for fracture stability was routinely performed on patients by the treating physicians and documented it in the medical record. Patients were followed until union or surgery for persistent fracture mobility. Main Outcome Measurements: Stability was graded if there was motion at the site (1: motion of any kind and 0: moved as a unit). Results: Seventy-three patients (87%) healed their fracture within our study cohort by 6 months postfracture. Of the remaining 11 patients, after discussion with their treating physicians about the option of surgical intervention, 8 chose to undergo open reduction internal fixation at an average of 8 months, 1 proceeded nonsurgical interventions, and 2 were lost of follow-up. If the humeral shaft fracture site was mobile at 6 weeks follow-up visit, it identified future fracture nonunion with 82% sensitivity and 99% specificity (only 1 patient with motion at 6 weeks proceeded to fracture union). Conclusion: With a high negative predictive value, clinical examination of fracture motion at 6 weeks should be assessed in every patient to determine which patients should obtain closer follow-up for the risk of nonunion progression. Knowledge of gross fracture motion can be used in the shared decision-making model in counseling about early surgical options. Level of Evidence: Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.
Cognitive and Affection Reform in Urban Elementary Schools: Listening to the Voices of Children.

This heuristic narratological inquiry used video-taped interviews and observations to explore the experiences of 145 urban students in grades one through six who also represented diverse cultural and linguistic backgrounds. The goal of the study was to identify curricular and pedagogical strategies that enhance what students love about school, while diminishing factors they disliked. Digital video recording captured students’ voices as “stories about school,” which provided thick description for analysis and interpretation. Through heuristic inquiry, the researchers used their personal insights and experiences to identify themes that suggested elementary students in urban schools want instruction that is active and engaging, makes use of their strengths and talents, and involves them in making choices about what and how to learn. They want caring teachers who have high academic expectations and desire to know more about their individual cultures as well as the cultures of other people.
Large-scale question classification in cQA by leveraging Wikipedia semantic knowledge

With the flourishing of community-based question answering (cQA) services like Yahoo! Answers, more and more web users seek their information need from these sites. Understanding user's information need expressed through their search questions is crucial to information providers. Question classification in cQA is studied for this purpose. However, there are two main difficulties in applying traditional methods (question classification in TREC QA and text classification) to cQA: (1) Traditional methods confine themselves to classify a text or question into two or a few predefined categories. While in cQA, the number of categories is much larger, such as Yahoo! Answers, there contains 1,263 categories. Our empirical results show that with the increasing of the number of categories to moderate size, the performance of the classification accuracy dramatically decreases. (2) Unlike the normal texts, questions in cQA are very short, which cannot provide sufficient word co-occurrence or shared information for a good similarity measure due to the data sparseness. In this paper, we propose a two-stage approach for question classification in cQA that can tackle the difficulties of the traditional methods. In the first stage, we preform a search process to prune the large-scale categories to focus our classification effort on a small subset. In the second stage, we enrich questions by leveraging Wikipedia semantic knowledge to tackle the data sparseness. As a result, the classification model is trained on the enriched small subset. We demonstrate the performance of our proposed method on Yahoo! Answers with 1,263 categories. The experimental results show that our proposed method significantly outperforms the baseline method (with error reductions of 23.21%).
Correlation-Based Pruning of Stacked Binary Relevance Models for Multi-Label Learning

Binary relevance (BR) learns a single binary model for each different label of multi-label data. It has linear complexity with respect to the number of labels, but does not take into account label correlations and may fail to accurately predict label combinations and rank labels according to relevance with a new in- stance. Stacking the models of BR in order to learn a model that associates their output to the true value of each label is a way to alleviate this problem. In this paper we propose the pruning of the models participating in the stacking process, by explicitly measuring the degree of label correlation using the phi coefficient. Exploratory analysis of phi shows that the correlations detected are meaningful and useful. Empirical evaluation of the pruning approach shows that it leads to substantial reduction of the computational cost of stacking and occasional im- provements in predictive performance.
A FAST COMPRESSED SENSING OF 3 D MRI IMAGE RECONSTRUCTION FROM RESIDUAL SPARSE SIGNAL

In medical science, the resolution of each & every part is must which can be recover from the reduced frequency acquisition sequence. While studying, the some researchers drawn analysis that some part is missing at the time 3D reconstruction. Our aim to recover missing information. For this purpose several methods are proposed. The information achieve by pressing suitable constraints on the reconstructed image. Using the forward-Backward splitting approaches, first update the sequence nature & in the second stage uses the different nonlinear filtering strategy. By modifying the algorithm showing the fast & more stable result. It gives the optimize performance even the highly under sampled image sequence. The several experiments shows that the improvement in the high resolution performance. Keywords— Compress Sensing, Splitting Approach, Total Minimum Variation, Filtering Strategy.
Lacosamide adjunctive therapy for partial-onset seizures: a meta-analysis

Background. The relative efficacy and safety of lacosamide as adjunctive therapy compared to other antiepileptic drugs has not been well established. Objective. To determine if lacosamide provides improved efficacy and safety, reduced length of hospital stay and improved quality of life compared with other anti-epileptic therapies for adults with partial-onset seizures. Data Sources. A systematic review of the medical literature using Medline (1946–Week 4, 2012), EMBASE (1980–Week 3, 2012), Cochrane Central Register of Controlled Trials (Issue 1 of 12, January 2012). Additional studies were identified (through to February 7, 2012) by searching bibliographies, the FDA drug approval files, clinical trial registries and major national and international neurology meeting abstracts. No restrictions on publication status or language were applied. Study Selection. Randomized controlled trials of lacosamide in adults with partial-onset seizures were included. Data Extraction. Study selection, extraction and risk of bias assessment were performed independently by two authors. Authors of studies were contacted for missing data. Data Synthesis. All pooled analyses used the random effects model. Results. Three trials (1311 patients) met inclusion criteria. Lacosamide increased the 50% responder rate compared to placebo (RR 1.68 [95% CI 1.36 to 2.08]; I2 = 0%). Discontinuation due to adverse events was statistically significantly higher in the lacosamide arm (RR3.13 [95% CI 1.94 to 5.06]; I2 = 0%). Individual adverse events (ataxia, dizziness, fatigue, and nausea) were also significantly higher in the lacosamide group. Limitations. All dosage arms from the included studies were pooled to make a single pair-wise comparison to placebo. Selective reporting of outcomes was found in all of the included RCTs. Conclusions. Lacosamide as adjunctive therapy in patients with partial-onset seizures increases the 50% responder rate but with significantly more adverse events compared to the placebo.
10H-3,6-Diazaphenothiazine induces G2/M phase cell cycle arrest and caspase-dependent apoptosis and inhibits cell invasion of A2780 ovarian carcinoma cells through the regulation of NF-κB and (BIRC6-XIAP) complexes

The asymptomatic properties and high treatment resistance of ovarian cancer result in poor treatment outcomes and high mortality rates. Although the fundamental chemotherapy provides promising anticancer activities, it is associated with severe side effects. The derivative of phenothiazine, namely, 10H-3,6-diazaphenothiazine (PTZ), was synthesized and reported with ideal anticancer effects in a previous paper. In this study, detailed anticancer properties of PTZ was examined on A2780 ovarian cancer cells by investigating the cytotoxicity profiles, mechanism of apoptosis, and cell invasion. Research outcomes revealed PTZ-induced dose-dependent inhibition on A2780 cancer cells (IC50 =0.62 µM), with significant less cytotoxicity toward HEK293 normal kidney cells and H9C2 normal heart cells. Generation of reactive oxygen species (ROS) and polarization of mitochondrial membrane potential (ΔΨm) suggests PTZ-induced cell death through oxidative damage. The RT2 Profiler PCR Array on apoptosis pathway demonstrated PTZ-induced apoptosis via intrinsic (mitochondria-dependent) and extrinsic (cell death receptor-dependent) pathway. Inhibition of NF-κB and subsequent inhibition of (BIRC6-XIAP) complex activities reduced the invasion rate of A2780 cancer cells penetrating through the Matrigel™ Invasion Chamber. Lastly, the cell cycle analysis hypothesizes that the compound is cytostatic and significantly arrests cell proliferation at G2/M phase. Hence, the exploration of the underlying anticancer mechanism of PTZ suggested its usage as promising chemotherapeutic agent.
Compact high-Q, low-loss mmW transmission lines and power splitters in RF CMOS technology

A set of slow-wave transmission lines designed in the BiCMOS9-MW technology from STMicroelectronics is characterized up to 110 GHz. State-of-the-art measured quality factors are obtained: 40 at 60 GHz, and more than 50 at 90 GHz. These transmission lines are also quite compact, thanks to a high effective relative permittivity, from 8 to 15 for a characteristic impedance, respectively between 70 Ω and 30 Ω. Based on these results, two power dividers using these slow-wave transmission lines are designed at millimeter-wave frequencies (77 GHz and 94 GHz). The measured S parameters present insertion loss lower than −3.5 dB and return loss better than −20 dB, at the operating frequency. Moreover the use of slow-wave transmission lines leads to a miniaturization of 70 % compared to a classical power splitter designed with microstrip or CPW transmission lines.