Miracle-dz/UniFAR-FTUs huggingface dataset:
HiTRACE-Web: an online tool for robust analysis of high-throughput capillary electrophoresis

To facilitate the analysis of large-scale high-throughput capillary electrophoresis data, we previously proposed a suite of efficient analysis software named HiTRACE (High Throughput Robust Analysis of Capillary Electrophoresis). HiTRACE has been used extensively for quantitating data from RNA and DNA structure mapping experiments, including mutate-and-map contact inference, chromatin footprinting, the Eterna RNA design project and other high-throughput applications. However, HiTRACE is based on a suite of command-line MATLAB scripts that requires nontrivial efforts to learn, use and extend. Here, we present HiTRACE-Web, an online version of HiTRACE that includes standard features previously available in the command-line version and additional features such as automated band annotation and flexible adjustment of annotations, all via a user-friendly environment. By making use of parallelization, the on-line workflow is also faster than software implementations available to most users on their local computers. Free access: http://hitrace.org.
Identification of human musculo-tendon subject specific dynamics using musculo-skeletal computations and non linear least square

The human body dynamics is very complex because of the number of degrees of freedom and of the number of muscles. Moreover the behavior of muscles is non-linear and subject specific. This paper presents an original method to estimate the subject specific dynamic parameters of the human muscles. A dynamic model of muscle, commonly used by the biomechanics community, is first presented. Then the force-activity-length-velocity relation is given. An application with four muscles, to the flexion/extension of the elbow joint is proposed. The human arm dynamics is analyzed in a motion capture studio for specific movements that excite the muscle dynamics. The acquisition of movements allows to compute the inverse kinematics and the inverse dynamics using a musculo-skeletal model of the human body, and finally the muscle force is estimated (input of the dynamic model of the muscle). The activity (also input of the dynamic model of the muscle) is measured using electromyography (EMGs) for the superficial muscles. The subject specific parameters of those muscles are then estimated by the non-linear least square method with Newton-Gauss algorithm. Experimental results obtain for valid subject are given
A Method and Tool for Managing Process Diversity in an Industrial Setting

Process diversity is necessary as soon as different components are integrated to one product. Diversity means that within the organizational process, the option to tailor and select the respective applicable process elements is maintained. If organizational learning and synergy are desired, managed process diversity is the answer. The paper describes the approach used in Alcatel’s Switching and Routing Business Division to manage process diversity. The article outlines the needs, evaluates the commercially available process and workflow modeling tools, and finally describes the methodology and tool support introduced in Alcatel over the past 2 years. Experiences as well as the benefits are discussed.
Measurements of parameters that affect voice quality over IEEE 802 . 11 b networks Literature Study

In this thesis we will perform real time voice meas urements over IEEE 802.11b wireless networks. The goals of these measurements are to as sess whether there are significant issues with 802.11b wireless networks regarding rea l-time voice communication. In particular we will investigate the behaviour and pe rformance of the network at the MAC layer in conjunction with the application layer, wh en a packet loss is reported. Similarly, we will look at how the 802.11b MAC protocol affect s the jitter and delay of real-time voice. With this information we intend to obtain a better understanding of wireless networks' suitability for voice communication. The data gathered as part of these investigations will be made available for further s tudy.
Damage Detection of Bridges : Experimental Research with Strain Mode Approach

This paper describes authors’ experimental research on damage detection of bridges by using strain mode technique. Experiments with two freely supported beams and a cable-stayed bridge were reported. Key in these experiments was to determine the dynamic strain by displacement measurement approach. Experimental results demonstrated that the dynamic strain could be gained under steady-state vibration very well. Under the non-steady-state vibration, results were not satisfactory. Higher instrumentation requirements were proposed. Finally some issues about damage detection of bridges while using strain mode approach were discussed.
Adding carotid total plaque area to the Framingham risk score improves cardiovascular risk classification

Introduction Cardiovascular events (CE) due to atherosclerosis are preventable. Identification of high-risk patients helps to focus resources on those most likely to benefit from expensive therapy. Atherosclerosis is not considered for patient risk categorization, even though a fraction of CE are predicted by Framingham risk factors. Our objective was to assess the incremental value of combining total plaque area (TPA) with the Framingham risk score (FramSc) using post-test probability (Ptp) in order to categorize risk in patients without CE and identify those at high risk and requiring intensive treatment. Material and methods A descriptive cross-sectional study was performed in the primary care setting in an Argentine population aged 22–90 years without CE. Both FramSc based on body mass index and Ptp-TPA were employed in 2035 patients for risk stratification and the resulting reclassification was compared. Total plaque area was measured with a high-resolution duplex ultrasound scanner. Results 57% male, 35% hypertensive, 27% hypercholesterolemia, 14% diabetes. 20.1% were low, 28.5% moderate, and 51.5% high risk. When patients were reclassified, 36% of them changed status; 24.1% migrated to a higher and 13.6% to a lower risk level (κ index = 0.360, SE κ = 0.16, p < 0.05, FramSc vs. Ptp-TPA). With this reclassification, 19.3% were low, 18.9% moderate and 61.8% high risk. Conclusions Quantification of Ptp-TPA leads to higher risk estimation than FramSc, suggesting that Ptp-TPA may be more sensitive than FramSc as a screening tool. If our observation is confirmed with a prospective study, this reclassification would improve the long-term benefits related to CE prevention.
Eliciting Accurate Responses to Consumption Questions among Idps in South Sudan Using “Honesty Primes”

Misreporting is a well-known challenge for researchers in social sciences. This issue is especially prevalent if incentives for misreporting exist, for example, to claim certain benefits or hide illegal behavior. Internally displaced persons are a population that is highly dependent on aid receipts and, thus, have strong incentives to underreport consumption levels. To improve reporting for such vulnerable populations, this paper proposes to integrate “honesty primes” into the consumption module of the questionnaire. Honesty primes are unconscious stimuli that induce a certain cognition or behavior. The study assesses the effectiveness of a bundle of randomly assigned primes within a sample of internally displaced persons in South Sudan. In line with the main hypothesis, positive and significant effects arise for low consumption quantiles, especially consumption quantities that are more susceptible to manipulation. Hence, honesty primes can act as a cost-effective tool to induce more accurate reporting. Further research is needed to identify more effective primes for the respective population of interest.
Fuzzy Multi-Join and Top-K Query Model for Search-As-You-Type in Multiple Tables

— A search-as-you-type system determines answers on-the-fly as a user types in a keyword query, character by character. There arises a higher need to know the support search-as-you-type on data residing in a relational DBMS. The existing work on keyword query focuses on to support type of search using the native database SQL. The leverage existing database functionalities is to meet high performance requirement to achieve an interactive speed. It uses auxiliary indexes that are stored as tables to increase search performance. But the main drawbacks in existing work were that it handle search as you type for databases for single table at the same time multiple tables were not taken into consideration. The Proposed work presents a Fuzzy Multi-Join technique to support multiple tables for search as-you-type in relational databases. Further the proposal presents a Top-K Query Search model to support ranking queries for search as-you-type in relational databases Top-k join queries are generated in relational query processors.
Prescient paper: multimedia document creation with document image matching

A system is described for creating paper documents that show images of presentation slides and bar codes that point to a multimedia recording of a presentation that has not yet occurred. An image-matching algorithm applied after a presentation determines when each slide was displayed. These time-stamps map the bar codes onto commands that control a multimedia player. We describe the system infrastructure that allows us to prepare such prescient documents and the document image-matching algorithm that enables the mapping of bar codes onto the times when slides were displayed. This provides a multimedia annotation tool that requires no electronic device at capture time.
What makes data robust: a data analysis in learning to rank

When applying learning to rank algorithms in real search applications, noise in human labeled training data becomes an inevitable problem which will affect the performance of the algorithms. Previous work mainly focused on studying how noise affects ranking algorithms and how to design robust ranking algorithms. In our work, we investigate what inherent characteristics make training data robust to label noise. The motivation of our work comes from an interesting observation that a same ranking algorithm may show very different sensitivities to label noise over different data sets. We thus investigate the underlying reason for this observation based on two typical kinds of learning to rank algorithms (i.e.~pairwise and listwise methods) and three different public data sets (i.e.~OHSUMED, TD2003 and MSLR-WEB10K). We find that when label noise increases in training data, it is the \emph{document pair noise ratio} (i.e.~\emph{pNoise}) rather than \emph{document noise ratio} (i.e.~\emph{dNoise}) that can well explain the performance degradation of a ranking algorithm.
Stress, pauses, pronominal types and pronominal functions in Danish spoken data

In this paper we present a study of the relation between types of third personal singular neuter pronoun and their functions in Danish spoken data where stress information is marked so that personal and demonstrative occurrences of the pronouns can be distinguished. This study confirms that there are language specific differences in the way various types of pronoun are used to refer to abstract entities in Danish compared to English data. In the second part of the paper we describe supervised machine learning experiments with the purpose of investigating the role of stress and pause information on the automatic recognition of the pronominal functions of third personal singular neuter pronouns in spoken corpora. The results of these experiments show that the inclusion of stress and pause information in the data improves classification.
The Risk Factors for Nosocomial Infection in Chinese Patients with Active Rheumatoid Arthritis in Shanghai

Objective. To analyse the potential risk factors of nosocomial infections in patients with active rheumatoid arthritis (RA). Methods. A total of 2452 active RA patients at Hospitals in Shanghai between January 2009 and February 2011 were analyzed. Their demographic and clinical characteristics were compared with those without infection, and the potential risk factors were determined by logistic regression analysis. Results. Multivariate analysis indicated the gender (OR = 0.70, 95% CI 0.53–0.92), duration in hospital (OR = 1.03 , 95%CI 1.01–1.05), number of organs involved (OR = 0.82, 95%CI 0.72–0.92), number of disease-modifying antirheumatic drugs ((DMARDs) (OR = 1.22, 95%CI 1.061–1.40)), corticosteroid therapy (OR = 1.02, 95%CI 1.01–1.03), peripheral white blood cell counts ((WBC) (OR = 1.04, 95%CI 1.00–1.08)), levels of serum albumin (OR = 0.98, 95%CI 0.97–0.99), and C-reactive protein ((CRP) (OR = 1.03 , 95%CI 1.01–1.04)) that were significantly associated with the risk of infections. Conclusion. The female patients, longer hospital stay, more organs involved, more DMARDs, corticosteroid usage, high counts of WBC, lower serum albumin, and higher serum CRP were independent risk factors of infections in active RA patients.
Ribonucleotide Reduction in Mycobacterium tuberculosis: Function and Expression of Genes Encoding Class Ib and Class II Ribonucleotide Reductases

ABSTRACT Mycobacterium tuberculosis, the causative agent of tuberculosis, possesses a class Ib ribonucleotide reductase (RNR), encoded by the nrdE and nrdF2 genes, in addition to a putative class II RNR, encoded by nrdZ. In this study we probed the relative contributions of these RNRs to the growth and persistence of M. tuberculosis. We found that targeted knockout of the nrdF2 gene could be achieved only in the presence of a complementing allele, confirming that this gene is essential under normal, in vitro growth conditions. This observation also implied that the alternate class Ib small subunit encoded by the nrdF1 gene is unable to substitute for nrdF2 and that the class II RNR, NrdZ, cannot substitute for the class Ib enzyme, NrdEF2. Conversely, a ΔnrdZ null mutant of M. tuberculosis was readily obtained by allelic exchange mutagenesis. Quantification of levels of nrdE, nrdF2, nrdF1, and nrdZ gene expression by real-time, quantitative reverse transcription-PCR with molecular beacons by using mRNA from aerobic and O2-limited cultures showed that nrdZ was significantly induced under microaerophilic conditions, in contrast to the other genes, whose expression was reduced by O2 restriction. However, survival of the ΔnrdZ mutant strain was not impaired under hypoxic conditions in vitro. Moreover, the lungs of B6D2/F1 mice infected with the ΔnrdZ mutant had bacterial loads comparable to those of lungs infected with the parental wild-type strain, which argues against the hypothesis that nrdZ plays a significant role in the virulence of M. tuberculosis in this mouse model.
Getting Over It

Little is known about whether emotion regulation can have lasting effects on the ability of a stimulus to continue eliciting affective responses in the future. We addressed this issue in this study. Participants cognitively reappraised negative images once or four times, and then 1 week later, they passively viewed old and new images, so that we could identify lasting effects of prior reappraisal. As in prior work, active reappraisal increased prefrontal responses but decreased amygdala responses and self-reported emotion. At 1 week, amygdala responses remained attenuated for images that had been repeatedly reappraised compared with images that had been reappraised once, new control images, and control images that had been seen as many times as reappraised images but had never been reappraised. Prefrontal activation was not selectively elevated for repeatedly reappraised images and was not related to long-term attenuation of amygdala responses. These results suggest that reappraisal can exert long-lasting “dose-dependent” effects on amygdala response that may cause lasting changes in the neural representation of an unpleasant event’s emotional value.
Automatic segmentation of brain MR images for patients with different kinds of epilepsy

Idiopathic generalized epilepsy (IGE) and symptomatic generalized epilepsy (SGE) are two kinds of generalized epilepsy. In this study, we discussed the methods of automatically segmentation of MR images for patients with these two kinds of epilepsy. K-Means clustering, expectation-maximization, and fuzzy c-means algorithms were employed to perform segmentation on brain images for patients with IGE. For patients with SGE, a trimmed likelihood estimator combined with Gaussian mixture model, which we improved based on other's existing work, was employed to detect obvious brain lesions on fluid-attenuated inversion recovery images. Gray matter, white matter, and cerebrospinal fluid were then segmented from the remaining normal brain part. Similarity metrics were used to evaluate the performance of the different segmentation methods. The Dice similarity coefficient of the segmentation results exceeded 70% and satisfied the basic clinical requirement. Actually, the segmentation results were acceptable to clinicians and can provide clinicians more disease information to diagnose and treat epilepsy.
ANN for Fast and Accurate Determination of Resonant Frequency and Quality Factor for CSSRR

Artificial Neural Network (ANN) has been employed for determination of resonant frequency and quality factor for complementary split ring resonator (CSSRR). It has been observed that as the number of training samples increases, the testing error decreases. The error in testing also decreases with the increase in the number of neurons in the hidden layer of the three layer multi-layer perceptron (MLP) network. The computational efficiency for this approach is very high in comparison to electromagnetic (EM) techniques, which takes more time. Usually it takes less than a second for training and testing the 60 samples using ANN whereas the EM simulator takes few hours to generate a single data, which is a huge gain in terms of computational efficiency. The accuracy of this approach is also very close to the EM simulation technique within 1%-2% errors.
Supporting Multiple User Types with a Multimodal Dialog Agent

Recent research has addressed the problem of formulating a dialog agent as a partially observable Markov decision process (POMDP), and learning a dialog policy that is optimal given the particular characteristics of the transition, observation and reward functions of the POMDP. This paper addresses the problem of trying to learn a small set of dialog agent policies that provide near-optimal behavior over a wide range of variations in POMDPs, reflecting different user preferences and environment characteristics. We show for a very simple dialog, we can cover a large number of simulated users to within 10% of their optimal return using fewer than 5% of the individual optimal policies.
Varactor-based signal restoration for near-speed-of-light surfing global interconnect

Varactors can be used to control delays and limit ISI-related signal integrity degradation for on-chip global interconnect. This paper presents a varactor-based “near-speed-of-light” interconnect design. In this design, the varactors compensate for delay variations enabling a simple, source-synchronous solution for clock-and-data recovery. Furthermore, the varactors provide pulse shaping that reduces ISI. We implemented these interconnects in the TSMC 90 nm CMOS process and present test results for 16 mm long lines.
A tool for lung nodules analysis based on segmentation and morphological operation

Lung cancer has become the foremost cause of high mortality rate in the western world. Subsequently, many image databases are being developed to enhance the investigation of nodules detection techniques in the early stages of lung cancer. Notably, Lung Image Database Consortium (LIDC) is widely recognized as one of the truthful databases but the efficient assessment of nodules on LIDC based Computed Tomography (CT) slices remains challenging. A novel, simple to use and efficient computer based application is proposed for the visualization, marking and segmentation of nodules on lung CT images. Greyscale thresholding and morphological operation (erosion) are applied to achieve adequate nodules segmentation on CT images. Furthermore, an extension enabling 3D visualization is also introduced. This application provides a solution to the challenges related to the segmentation and analysis of nodules in the CT images.
Least Mean Squares Estimation of Graph Signals

In many applications spanning from sensor to social networks, transportation systems, gene regulatory networks or big data, the signals of interest are defined over the vertices of a graph. The aim of this paper is to propose a least mean square (LMS) strategy for adaptive estimation of signals defined over graphs. Assuming the graph signal to be band-limited, over a known bandwidth, the method enables reconstruction, with guaranteed performance in terms of mean-square error, and tracking from a limited number of observations over a subset of vertices. A detailed mean square analysis provides the performance of the proposed method, and leads to several insights for designing useful sampling strategies for graph signals. Numerical results validate our theoretical findings, and illustrate the performance of the proposed method. Furthermore, to cope with the case where the bandwidth is not known beforehand, we propose a method that performs a sparse online estimation of the signal support in the (graph) frequency domain, which enables online adaptation of the graph sampling strategy. Finally, we apply the proposed method to build the power spatial density cartography of a given operational region in a cognitive network environment.
Politics by Other Means: Law in the Struggle against Apartheid, 1980-1994

1. Glossary of Acronyms and Foreign Words 2. Speaking with the Ogre 3. Politics by Other Means? 4. Carving Loopholes in the Pass Laws 5. White Resistance to the Military 6. Seeking Recognition 7. Mpophomeni and the War in Ntal 8. State Terrorism:The Response of Law and Medicine to Police Torture 9. Censorship and the Closure of the New Nation 10. The Alexandra Treason Trial 11. Bakwena ba Magopa:The Last Forced Removal 12. Moutse and KwaNdebele:Ethnicity and Gender in the Challange to Grand Apartheid 13. Disestablishing Oukasie 14. The Roles of Law
FEUP at TREC 2010 Blog Track: Using h-index for blog ranking

This paper describes the participation of FEUP, from the University of Porto, in the TREC 2010 Blog Track. FEUP participated in the baseline blog distillation task with work focused on the use of link features available in the TREC Blogs08 collection. The approach presented in this paper uses the link information available in most individual posts to amplify each post’s score. Blog scores, and subsequent ranks, are obtained by combining individual post scores. We boost post scores using the in-degree of each post and the h-index of each blog. This results in an improvement of P@10, over our baseline, for the in-degree and the h-index runs. When compared to the indegree, the h-index run results in higher performance values for each of the applied evaluation metrics.